The authors have described a method of streamflow prediction through the use of the exceedance probability concept. The basis of the concept is identical with the flow duration curve surface hydrology (Cigizoglu and Bayazit 2000). In both the development and the application of their methodology there are some vague considerations and points that need further clarification. It is true that the reliable estimation of streamflows is necessary in any water resources development and management. However, from meteorological and atmospheric views, this paper should try and search for such a prediction for the precipitation records. For the major hydrological variable streamflow is shaped by topographic, morphologic, vegetative, and surface geological characteristics of a region based on the precipitation event occurrences.

This paper presents a triple diagram method (TDM) based on the Kriging technique for predicting future lake levels from two antecedent measurements, which are considered as independent variables. The experimental semivariograms (SV) for three lags are obtained, and the most suitable theoretical SV for the three cases is the Gaussian type. Based on these theoretical SVs, the contour lines of the dependent variable are constructed by Kriging. The resulting maps are referred to as the TDM model for lake level fluctuation. It is expected that this model will be used more extensively than the Markov or ARIMA (AutoRegressive Integrated Moving Average) models commonly available for stochastic modelling and predictions. The TDM does not have restrictive assumptions such as the stationarity and ergodicity which are preliminary requirements for the stochastic modelling. The TDM is applied to monthly level fluctuations of Lake Van in eastern Turkey. In the prediction procedure lags, one, two and three are considered. Interpretations from these three basic diagrams help to identify properties of lake level fluctuations. It is observed that the TDM preserves the statistical properties. These diagrams also help to make predictions with less than 10 % relative error.

Persistence is the most important property in any hydrologic design concerning the storage capacity of reservoirs, average return periods, failure risks, and drought properties. Its consideration in analytical derivations of design criteria presents difficulties, especially in autocorrelated hydrologic processes, and for this reason, most often the analytical expressions are obtained on the basis of non-persistent (independent) cases. Although the conventional autocorrelation coefficients and function are used in many hydrological design problems, the very definition of the autocorrelation function requires that the underlying hydrologic process generating mechanism abide with normal (Gaussian) probability distribution function in addition to other restrictive assumptions. Since almost all of the analytical stochastic approaches are based on the normality assumption, it is necessary to transform non-Gaussian distributions to the normal distribution to use analytical expressions. During the transformation process, the very genuine persistence property of the basic hydrologic variables is not preserved, even when the statistical parameters such as the average, standard deviation, skewness coefficient, kurtosis, etc., are maintained in the transformed data. This shortcoming in the autocorrelation function is by-passed by the introduction and use of an autorun function, which is probability distribution free and a robust parameter. Its basis is the conditional probability statement which does not require any assumptions in practical applications. Various practical and simple hydrological design quantities are developed on the basis of the autorun coefficients without considering the conventional autocorrelation structure. The application of methodology is presented for John’s river in Florida.

DOI: 10.1061/(ASCE)1084-0699(2003)8:6(329)

CE Database subject headings: Hydrology; Frequency; Risk; Probability; Water storage; Design.

The quality of ground water in any aquifer takes its final form due to natural mixture of waters, which may originate from different sources. Water quality varies from one aquifer to another and even within the same aquifer itself. Different ground water quality is obtained from wells and is mixed in a common reservoir prior to any consumption. This artificial mixing enables an increase in available ground water of a desired quality for agricultural or residential purposes. The question remains as to what proportions of water from different wells should be mixed together to achieve a desired water quality for this artificial mixture. Two sets of laboratory experiments were carried out, namely, the addition of saline water to a fixed volume of fresh water. After each addition, the mixture volume and the electric conductivity value of the artificially mixed water were recorded. The experiments were carried out under the same laboratory temperature of 20°C. A standard curve was developed first experimentally and then confirmed theoretically. This curve is useful in determining either the volume or discharge ratio from two wells to achieve a predetermined electrical conductivity value of the artificial mixture. The application of the curve is given for two wells within the Quaternary deposits in the western part of the Kingdom of Saudi Arabia

Rainfall-runoff relationships are widely used in many engineering hydrologic designs in urban and rural areas. Such relationships are obtained through the application of regression analysis in many studies. Unfortunately, in the classical regression approach to determine rainfall-runoff relationships, internal uncertainties are not taken explicitly into consideration. In this paper, an alternative to the classical regression approach is proposed through fuzzy system modeling. It is concluded that the fuzzy systems approach yields comparatively less relative error than a regression approach and, therefore, it is recommended for use in future. The application is presented for rainfall-runoff records at two sites near Istanbul, Turkey.

The statistical behavior of wave energy at a single site is derived by considering simultaneous variations in the period and wave height. In this paper, the general wave power formulation is derived by using the theory of perturbation. This method leads to a general formulation of the wave power expectation and other statistical parameter expressions, such as standard deviation and coefficient of variation. The statistical parameters, namely the mean value and variance of wave energy, are found in terms of the simple statistical parameters of period, significant wave height and zero up-crossing period. The elegance of these parameters is that they are distribution free. These parameters provide a means for defining the wave energy distribution function by employing the Chebyschev’s inequality. Subsequently, an approximate probability distribution function of the wave energy is also derived for assessment of risk and reliability associated with wave energy. Necessary simple charts are given for risk and reliability assessments. Two procedures are presented for such assessments in wave energy calculations and the applications of these procedures are provided for wave energy potential assessment in the regions of the Pacific Ocean off the west coast of U.S. ©2003 Elsevier Ltd. All rights reserved.

The wave energy potential is directly proportional to the wave period and second power of wave height averaged over a suitable time period. The wave height and period have temporal and spatial stochastic variations. It is the main purpose of this paper to derive the most general wave energy formulation by considering simultaneously the temporal variations both in the wave height and period. The correction factor is derived explicitly in terms of cross-correlation and the coefficients of variation. The application of the methodology is performed for wave measurement stations located in the Pacific Ocean off the west coast of the US

Significant wave height estimates are necessary for many applications in coastal and offshore engineering and therefore various estimation models are proposed in the literature for this purpose. Unfortunately, most of these models provide simultaneous wave height estimations from wind speed measurements. However, in practical studies, the prediction of significant wave height is necessary from previous time interval measurements. This paper presents a dynamic significant wave height prediction procedure based on the perceptron Kalman filtering concepts. Past measurements of significant wave height and wind speed variables are used for training the adaptive model and it is then employed to predict the significant wave height amounts for future time intervals from the wind speed measurements only. The verification of the proposed model is achieved through the dynamic significant wave height and wind speed time series plots, observed versus predicted values scatter diagram and the classical linear significant wave height models. The application of the proposed model is presented for a station in USA

The El Nino Southern Oscillation (ENSO) template concept is presented for seasonal streamflow prediction methodology by considering the Southern Oscillation Index (SOI) and Sea Surface Temperature (SST) variables. The methodology provides linguistic and quantitative inference capabilities. The prediction model uses the geostatistical (Kriging) technique. Each ENSO template has nine categories including one with high SST and low SOI values that represents the El Nino event. Similarly, the category with low SST and high SOI values depicts the La Nina event. The application of the methodology is presented for the seasonal streamflow records in the southeastern part of the Australian continent along the Pacific Ocean. April–September streamflow is predicted by using five different lead times including 3-month ENSO indicator averages. The seasonal streamflow predictions at different lag times are obtained given the values of SST and SOI. The overall relative prediction error is rather small at about 13%. The bigger the lag the bigger is the prediction error. However, the relative error between averages of observation and prediction values is less than 5%. Similar ENSO templates can be used for streamflow prediction in other parts of the world.

DOI: 10.1061/(ASCE)1084-0699(2004)9:5(368)

CE Database subject headings: Steamflow; Predictions; Seasonal variations; Models.

Abstract:

Predictions of the discharge and the associated sediment concentration are very useful ingredients in any water resources reservoir design, planning, maintenance, and operation. Although there are many empirical relationships between the discharge and sediment concentration amounts, they need estimation of model parameters. Generally, parameter estimations are achieved through the regression method (RM), which has several restrictive assumptions. Such models are locally valid and their structures and parameter values are questionable from region to others. This paper proposes a new approach for sediment concentration prediction provided that there are measurements of discharge and sediment concentration. The basis of the methodology is a dynamic transitional model between successive time instances based on two variables, namely, discharge and sediment concentration measurements. The transition matrix elements are estimated from the measurements through a special form of the artificial neural networks as perceptrons. The sediment concentration predictions from discharge measurements are achieved through a perceptron Kalman filtering (PKF) technique. In the meantime, this technique also provides temporal predictions. A certain portion of the measurement sequence is employed for the model parameter estimations through training and the remaining part is used for the model verification. Detailed comparisons between RM and PKF approaches are presented and, finally, it is shown that the latter model works dynamically by simulating the observation scatter diagram in the best possible manner with smaller prediction errors. The application of the methodology is performed for the discharge and sediment concentration measurements obtained from the Mississippi River basin at St. Louis, Missouri. It is found that the PKF methodology has smaller average relative, root-mean-square, and absolute errors than RM. Furthermore, graphical representation, such as the scatter and frequency diagrams, indicated that the PKF approach has superiority over the RM.

DOI: 10.1061/(ASCE)0733-9429(2004)130:8(816)

CE Database subject headings: Discharge measurement; Concentration; Kalman filter; Predictions; Sedimentation.

Abstract.

This paper presents a Takagi Sugeno (TS) fuzzy method for predicting future monthly water consumption values from three antecedent water consumption amounts, which are considered as independent variables. Mean square error (MSE) values for different model configurations are obtained, and the most effective model is selected. It is expected that this model will be more extensively used than Markov or ARIMA (AutoRegressive Integrated Moving Average) models commonly available for stochastic modeling and predictions. The TS fuzzy model does not have restrictive assumptions such as the stationarity and ergodicity which are primary requirements for the stochastic modeling. The TS fuzzy model is applied to monthly water consumption fluctuations of Istanbul city in Turkey. In the prediction procedure only lag one is considered. It is observed that the TS fuzzy model preserves the statistical properties. This model also helps to make predictions with less than 10% relative error.

Key words: fluctuation, fuzzy logic, Markov, prediction, water consumption

Spatial assessment of variables in a considered region saves its significance for engineering applications. Branches in ocean engineering need the results of this assessment like radius of influence of stations that records the wave measurements and various meteorological variables values. Classical approaches like Kriging do not provide radius of influence for the concerned station. On the other hand, prediction of these measurements from other surrounding stations in the region is a basic requirement. In this paper, it is aimed to predict significant wave height records in a specific region by using trigonometric point cumulative semivariogram (TPCSV) concept. The main difference of this approach from the point cumulative semivariogram (PCSV) approach is the determination of influence radius. More accurate results can be obtained by TPCSV. The spatial correlation and weightings are also obtained through the TPCSV where the distance between two sites is known. The proposed method yields the least prediction error compared with other objective methodologies. The implementation of this methodology is presented for a set of offshore locations distributed along the west United States coastline.

Keywords: Area of influence; Estimation; Interpolation; Objective analysis; Radius of influence; Semivariogram; Significant wave height

Spatial assessment of variables in a considered region saves its significance for engineering applications. Branches in ocean engineering need the results of this assessment like radius of influence of stations that records the wave measurements and various meteorological variables values. Classical approaches like Kriging do not provide radius of influence for the concerned station. On the other hand, prediction of these measurements from other surrounding stations in the region is a basic requirement. In this paper, it is aimed to predict significant wave height records in a specific region by using trigonometric point cumulative semivariogram (TPCSV) concept. The main difference of this approach from the point cumulative semivariogram (PCSV) approach is the determination of influence radius. More accurate results can be obtained by TPCSV. The spatial correlation and weightings are also obtained through the TPCSV where the distance between two sites is known. The proposed method yields the least prediction error compared with other objective methodologies. The implementation of this methodology is presented for a set of offshore locations distributed along the west United States coastline.

Keywords: Area of influence; Estimation; Interpolation; Objective analysis; Radius of influence; Semivariogram; Significant wave height

Abstract:

The main purpose of this paper is to propose a standard regional dependence function (SRDF) based on concepts of semivariogram and especially point cumulative semivariogram for regional streamflow estimation. The SRDFs are obtained from available spatial data and show regional dependence, which decreases with distance from a given site. These functions present quantitatively the regional dependence of the streamflow phenomenon recorded at irregular sites over a drainage basin and provide a unique opportunity for the establishment of regional objective estimation method based on weighed averages. The weightings are obtained by means of the SRDF given the distance between any two sites. The implementation of the proposed methodology is presented for some streamflow records from the Lower and Upper Mississippi River watershed in the United States. For the application, the experimental SRDF forms are first obtained from the available data, and these are then employed directly in the regional estimation procedure. The study indicated that the use of all the stations in a region for the estimation at any particular station is rather naïve because far away stations (more than 1,000 km away) are taken into consideration. The final conclusion is that discharge at any particular station is better described as a function of discharge at several (3–5) closest stations. The reliability of the method is measured through the cross validation procedure, and it is observed that the procedure yields streamflow predictions with less than 10% relative error.

CE Database subject headings: Streamflow; Forecasting; Regional analysis; Mississippi River.

Significant wave height is a very important variable used in ocean engineering studies. Spatial variation of the significant wave height is very important for wave energy abstraction studies which is highly dependent on the wave climate. Significant wave height and period are two of the most important wave features that directly affect the energy production. In nature these two variables exhibit temporal randomness and spatial changes. These variations cause heterogeneous regional dispersion of wave energy. In order to assess regional energy distribution, it is necessary to use the concept of regionalized variables through geostatistical methods. The main purpose of this paper is to evaluate the spatial characteristics of significant wave height by the point cumulative semivariogram approach leading to a standard regional dependence function (SRDF) based on concepts of the semivariogram. It is also possible to estimate the unmeasured station value from the closest stations by using SRDF and determine the optimum station intervals. The SRDFs are obtained from available spatial data decreasing with distance for a given set of sites. The wave energy resource in a region of the Pacific Ocean off the west coast of the United States has been evaluated as the application of proposed methodology. It is found that spatial modeling of the region considered can be achieved by using the SRDF function in acceptable error limits.

CE Database subject headings: Ocean waves; Spatial analysis; Wave energy; Weighing devices; Wave height; Wave climatology.

Abstract

The modern modeling techniques show a significant progress in recent years. Fuzzy logic approach is one of these methods that can be used for forecasting purposes and identification of complex systems. In this study, it is aimed to model monthly dissolved oxygen (DO) amount variations. Firstly, regression technique is used to remove the trend from the actual dissolved oxygen time series. Detrended time series is then modeled with Takagi–Sugeno Fuzzy logic approach. The monthly historical records of DO as considered in this paper provide a fundamental data exhibiting persistence. It is this dependence that gives opportunity for the serial modeling of the data sequence concerned. These models are the prime and sole means to derive the future likely excedences over the historical values and hence provide a chance for the future planner with decision variables such as the minimum DO amounts and their persistence for some duration. In the scope of this study, DO concentration changes in the two stations located in Golden Horn at the 0.5m (upper layer) depth were modeled. As a result of the study, it is seen that one can forecast the next month’s DO amount from antecedent measurements within an acceptable relative error limits.

Keywords: Concentration dissolved oxygen; Forecast; Fuzzy logic; Golden Horn

In practice, rainfall–runoff relationships are achieved through a simply defined runoff coefficient concept that is widely used in many engineering hydrological designs in urban and rural areas. The simplicity of the method, with the sole requirement of runoff coefficient assessment, is the main attractiveness, in addition to its successful prediction of average runoff rates for a given rainfall record. Unfortunately, in the classical regression approach of the rainfall–runoff relationship, internal variabilities are not taken into consideration explicitly. The runoff coefficient is considered a constant value, and it is used without distinction of antecedent conditions for the calculation of runoff from the rainfall record. In this paper, various other uncertainty embedded versions of the runoff coefficient, and hence rainfall–runoff formulation, are presented in terms of statistics, probability, perturbation and, finally, fuzzy system modelling. It is concluded that the fuzzy logic approach yields the least relative error among the various alternative runoff calculation methods; therefore, it is recommended for use in future studies. The application of various alternatives is presented for two monthly rainfall-runoff records around Istanbul, Turkey.

KEY WORDS fuzzy logic; perturbation; prediction; rainfall-runoff; hydrological modelling

Abstract

In this paper, a technique is proposed in order to study triple time series. It combines the variable of interest, sulfur dioxide (SO2) with two related meteorological variables. Hence, three variables measured at the same time points are jointly analyzed. Instead of using classical multiple time series analysis, it is suggested to consider the measurements of the two meteorological variables as coordinates of a two-dimensional space and the simultaneous observation of the third variable (associated SO2 concentrations) at each pair of coordinates. Subsequently, well-known optimum interpolation is used for predicting the SO2 concentrations on the basis of six meteorological variables. All the variables of the study are measured at the same times (all days in 2000) around the city of Istanbul, Turkey. The triple diagrams, in the form of contour maps, help to answer various questions concerning the SO2 concentration variability with respect to meteorological variables. The same diagrams also provide a basis for the prediction of SO2 concentrations. It is shown that the relative prediction error is less than 10%, which is acceptable for the practical studies.

Key words: Kriging, air pollution, optimum interpolation, prediction.

Abstract:

Scour at a downstream vertical gate of a dam is investigated using a fuzzy logic inference system. The application is presented for experimental data sets. A comparison is made between a regression formulation (RM) and a fuzzy logic approach. Some restrictive assumptions for RM are discussed. Here the Takagi–Sugeno (TS) approach is used with a constant function in the consequent part of the fuzzy rules. It is demonstrated that the TS model gives a lower error than the RM. Furthermore, scatter diagrams indicate that the TS approach has superiority over the RM.

CE Database subject headings: Scour; Fuzzy sets; Hydraulic engineering; Regression analysis; Gates; Dams.

The authors have presented a methodology based on artificial neural network (ANN) approach to estimate wave energy spectrum. The ANN model results then compared with the classical models such as JONSWAP, Pierson–Moskowitz (PM) and Scott spectrums.

The discussion shall focus on three main points that is not considered in the study. The first one is about the selection of input variables of the ANN model. The authors stated that the objective of the study is to derive wave energy spectrum from the wave parameters such as significant wave height, Hs, zero up-crossing period, Tz, spectral width, e and peakedness, Qp. While deriving the spectra these parameters are assumed to be known. However this assumption is not useful for practical applications. The use of Hs and Tz for design purposes may be practical but e and Qp cannot be easily interpreted by the user to extract a physical meaning.

Abstract

Lake Van in eastern Turkey has been subject to water level rise during the last decade and, consequently, the low-lying areas along the shore are inundated, giving problems to local administrators, governmental officials, irrigation activities and to people’s property. Therefore, forecasting water levels of the Lake has started to attract the attention of the researchers in the country. An attempt has been made to use artificial neural networks (ANN) for modeling the temporal change water levels of Lake Van. A back-propagation algorithm is used for training. The study indicated that neural networks can successfully model the complex relationship between the rainfall and consecutive water levels. Three different cases were considered with the network trained for different arrangements of input nodes, such as current and antecedent lake levels, rainfall amounts. All of the three models yields relatively close results to each other. The neural network model is simpler and more reliable than the conventional methods such as autoregressive (AR), moving average (MA), and autoregressive moving average with exogenous input (ARMAX) models. It is shown that the relative errors for these two different models, are below 10% which is acceptable for engineering studies. In this study, dynamic changes of the lake level are evaluated. In contrast to classical methods, ANNs do not require strict assumptions such as linearity, normality, homoscadacity etc.

Keywords Hydrologic budget . Lake level . Neural networks . Prediction

Abstract

Classical aquifer test models assume an isotropic and homogenous medium with Darcian flow as an ideal case. Deviations from type curves indicate the heterogeneity of the aquifer. There are heterogeneities even at small scales. There are also systematic variations which are not considered by type curves. For instance, due to the groundwater movement during the well-development phase, the hydraulic conductivity tends to decrease with radial distance from the well. For practical representation of such a systematic variation, a linear hydraulic conductivity decrease is adopted and the relevant type curve expressions are derived. These expressions are checked against the classical constant hydraulic conductivity solutions in the literature. Derived type curves are employed for the identification of aquifer parameters, namely transmissivity and the radial hydraulic conductivity variation parameters. The type curve expression derived transforms into the classical Thiem expression when the aquifer hydraulic conductivity is considered as constant. It is observed that classical steady-state flow with constant hydraulic conductivity underestimates the transmissivity by 10%.

Key words Darcy law; groundwater; steady-state flow; type curve; variable hydraulic conductivity

Summary

Lake Van is one of the largest terminal lakes in the world. In recent years, significant lake level fluctuations have occurred and can be related to global climatic change. This fluctuation sometimes exhibits abrupt shifts. Floods originating from the lake can cause considerable damage and loss in agriculture and urban areas. Therefore, water level forecasting plays a significant role in planning and design. This study is aimed at predicting future lake levels from past rainfall amounts and water level records. A dynamical change of the lake level is evaluated by the fuzzy approach. The fuzzy inference system has the ability to use fuzzy membership functions that include the uncertainties of the concerned event. This method is applied for Lake Van, in east Turkey. Furthermore, model capabilities are compared with ARMAX model. It is shown that lower absolute errors are obtained with the Takagi-Sugeno fuzzy approach than with the ARMAX model.

Prediction of wave parameters is very important for planning, designing and operation of ocean structures. Accurate estimation of these parameters provides engineers to construct more economical and reliable ocean structures such as harbors, breakwaters, oil production platforms and ocean wave energy converters. For this reason, optimum operation of these plants has become a must. Various methods have been introduced to determine the relation among wind speed previous and current wave parameters. Method proposed in this paper consists of genetic algorithms and Kalman filters which is called as Geno-Kalman filtering. It is based on adaptive calculation to reach the solution. Also a comparison has been made between perceptron Kalman filtering and Geno-Kalman filtering techniques. The application of Geno-Kalman filtering was performed for station 46002 which located in the Coos Bay at Oregon, USA. It is observed that the Geno-Kalman filtering methodology has smaller absolute, mean-square and relative errors than perceptron Kalman filtering. Also coefficient of efficiency value which was used to evaluate results between observed and estimated is higher at Geno-Kalman filtering than perceptron Kalman filtering.

Keywords: Adaptive modeling, Genetic algorithms, Kalman filtering prediction, Wave parameters

Kriging is one of the most developed methodologies in the regional variable modeling. However, one of its drawbacks is that the influence radius can not be determined by this method. In which distance and in what ratio that pivot station is influenced from adjacent sites is rather often encountered problem in practical applications. Regional weighting functions obtained from available data consist of several broken lines. Each line has different slopes which represent the similarity and the contribution of adjacent stations as a weighting coefficient. The approach in this study is called as Slope Regional Dependency Function (SRDF). The main idea of this approach is to express the variability in value differences and distances together. Originally proposed SRDF and Trigonometric Point Cumulative Semi-Variogram (TPCSV) methods are used to predict streamflow. TPCSV and Point Cumulative Semi-Variogram (PCSV) approaches are also compared with each other. Prediction performance of all the three methods revealed a relative error less than 10% which is acceptable for most engineering applications. It is shown that SRDF outperforms PCSV and TPCSV with very high differences. It can be used for missing data completion, determination of measurement sites location, calculation of influence radius, and determination of regional variable potential. The proposed method is applied for the 38 stream flow measurement sites located in the Mississippi River basin.

Abstract

A contour diagram approach is presented for the identification of surface ozone concentration feature based on a set of rules by considering the meteorological variables such as the solar radiation, wind speed, temperature, humidity and rainfall. A fuzzy rule system approach is used because of the imprecise, insufficient, ambiguous and uncertain data available. The contour diagrams help to identify qualitative ozone concentration variability rules which are more general than conventional statistical or time series analysis. In the methodology, ozone concentration contours are based on a fixed variable as ozone precursor, namely, NOx and as the third variable one of the meteorological factors. Such contour diagrams for ozone concentration variation are prepared for six months. It is possible to identify the maximum ozone concentration episodes from these diagrams and then to set up the valid rules in the form of IFTHEN logical statements. These rules are obtained from available daily ozone, NOx and meteorological data as a first approximate reasoning step. In this manner, without mathematical formulations, expert maximum ozone concentration systems are identified. The application of the contour diagram approach is performed for daily ozone concentration measurements on European side of Istanbul city. It is concluded that through approximate reasoning with fuzzy rules, the maximum ozone concentration episodes can be identified and predicted without any mathematical expression.

Key words: Contour diagrams, Fuzzy rules, fuzzy sets, Meteorology, non-parametric approach, ozone concentrations, Vagueness

Abstract

Accurate sediment load prediction is very important in planning, designing, operating and maintenance of water resources structures. Various models have been developed so far to identify the relation between discharge and sediment load. Most of the models based on regression method (RM) have some restrictive assumptions. This method is able to provide only one solution point for estimation of sediment amount. On the other hand, genetic algorithms (GAs) can produce more than one solution points providing optimal relation between discharge and sediment loads. Sediment load can be successfully predicted from discharge measurements by using GAs. Graphical and numerical data are presented to compare GAs with RM. GA methodology is applied to discharge and sediment load data obtained from Mississippi river at Missouri, St. Louis. It is found that GAs outperform RM in terms of mean relative error (MRE).

Keywords: Discharg, Genetic algorithms, Prediction, Sediment load

Classification of reach-scale morphology is useful as a shorthand descriptor of the geomorphic processes andaquatic habitat settings at particular river sections due to thetie between reach-scale form and fluvial and geologic processes. The value of such classifications motivates the development of practical, predictive classification models that operate on measured or predicted reach-scale hydraulic

and sediment conditions. Such models are useful for clas-sifying large sections of channel networks and identifying river sections that are particularly susceptible to changes in flow or sediment conditions which could result from climatic or anthropogenic disturbances [Wohl and Merritt,2005; Flores et al., 2006]. The focus of this paper is on the development of a classification models for mountain stream

reach morphology based on simple input variables that are linked to the underlying processes xpressed in the local morphology types using multilayer perceptron networks.

The determination of spatial dependency of regionalized variable (ReV) is important in engineering studies. Regional

dependency function that leads to calculation of weighting coef?cients is required in order to make regional or point-wise

estimations. After obtaining this dependency function, it is possible to complete missing records in the time series and locate

new measurement station. Also determination of regional dependency function is also useful to understand the regional variation

of ReV. Point Cumulative Semi-Variogram (PCSV) is another methodology to understand the regional dependency of ReV

related to the magnitude and the location. However, this methodology is not useful to determine the weighting coef?cient,

which is required to make regional and point-wise estimations. However, in Point Semi-Variogram (PSV) proposed here,

weighting coef?cient depends on both magnitude and location. Although the regional dependency function has a ?uctuating

structure in PSV approach, this function gradually increases with distance in PCSV. The study area is selected in Mississippi

river basin with 38 stream?ow stations used for PCSV application before. It is aimed to compare two different geostatistical

models for the same data set. PSV method has an ability to determine the value of variable along with optimum number of

neighbour stations and in?uence radius. PSV and slope PSV approaches are compared with the PCSV. It was shown that slope

slope point semi-variogram (SPSV) approaches had relative error below 5%, and PSV and PCSV methods revealed relative

errors below 10%. Copyright ? 2009 John Wiley & Sons, Ltd.

KEY WORDS streamflow; point-wise estimation; geostatistical analysis; optimal regional dependency function; point

semi-variogram

Contribution by A. Altunkaynak, Istanbul Technical University,

Turkey

In this paper the authors developed a fuzzy logic model to predict hydraulic jump aeration efﬁciency but parts of the paper appear to be ambiguous, there are points that may need further clariﬁcation in both the application of methodology and the results sections.

First, the most important step in developing a fuzzy logic model, which is a kind of blackbox model, is to train and test the model properly. For these operations, the available data are normally split into two parts. While approximately two-thirds of the total data are used for training, the rest of the data are left for testing. The authors of the paper do not appear to have followed this procedure. It may have been better if they had used the training data to determine the fuzzy rule based membership functions of model inputs (Fr1 and Re) and output by considering the least prediction errors and then validated the model using an independent data set such as test data. The absence of these procedures makes the results of the study questionable because it is a well known fact that validating a model with the data that are used at the same time for establishing the model produces high correlations coefﬁcients between observed and predicted values.

We aimed to investigate if the outcome of the patients with intracranial aneurysm could be predicted by

fuzzy logic approach. Two hundred and forty two patients with the diagnosis of intracranial aneurysm

were assessed retrospectively between January 2001 and December 2005. We recorded World Federation

of Neurological Surgeons Scale (WFNSS), Fisher Scale and age at admission and Glasgow Outcome Score

(GOS) at discharge from hospitalization for all the patients. We developed fuzzy sets by dividing WFNSS

into four groups as good, fair, bad and very bad; age into three groups as young, middle and old; Fisher

scale into three groups as few, moderate and large; outcome score into four groups as bad, fair, good and

very good. We calculated the outcome of the patient with these sets by fuzzy model. Predicted outcome

by fuzzy logic approach correlated with observed outcome scores of the patients (p > 0, 05), including 95%

confidence interval. We showed that outcome of the patients with aneurysm can be predicted by fuzzy

logic approach, accurately.

Keywords: Fisher scale, Fuzzy logic, Intracranial aneurysm, Outcome score, Prediction

Fuzzy logic models for time-variant speciﬁc ﬂuxes during crossﬂow microﬁltration of several feed

suspensions under a wide range of hydrodynamic parameters were derived and validated. The coefﬁcient

of efﬁciency values, which quantiﬁes the degree of agreement between experimental observations and

numerically calculated values were found greater than 0.96 for all cases. An important contribution of this

research is that it is demonstrated that a single robust fuzzy model can quantitatively capture cumulative

effects of a range of particle sizes on membrane fouling. Hence, empirical models incorporating fuzzy logical

operators appear to encompass overall effects of non-linear colloidal transport and deposition mechanisms

as well as changes in cake morphology and resistance with hydrodynamics better than mathematically

complicated mechanistic models. This also suggests the use of fuzzy logic algorithms in programmable

control systems for improved on-site operation of membrane-based liquid–solid separation employed in

municipalities and industries.

It is important to determine the amount of daily drinking water requirement for a person not only for the

health of people but also for the planning and management of the water resources. Physical activity, body weight and temperature play significant role in drinking water consumption rates. Human activity variables are most often given in crisp numerical interval classifications for water consumption calculations. The aim of this paper is to establish a fuzzy model for predicting the water consumption rates based on data at the hand. The fuzzy sets such as low, medium, high can be used to quantify vague, imprecise or incomplete descriptions which are collectively referred to as fuzzy data in the literature. Fuzzy model inputs are considered as the physical activity, body weight and temperature, whereas the output is the water consumption levels. The fuzzy sets are chosen in an appropriate manner and the prediction model of water consumption is compared with the actual consumption amounts. It is not possible to treat such linguistic fuzzy data by statistical methods. It is observed that the model predictions have less than 5% relative error. The model is tested with an independent data set for its successful prediction capability.

Keywords: Physical activity, Fuzzy model, Temperature, Uncertainty, Water consumption, Weight

Predicting water level fluctuations in a lake is crucial in terms of sustainable water supply planning, flood control, management of water resources, shoreline maintenance, sustainability of ecosystem, and economic development. This study developed a new predictive model based on the season algorithm (SA) and multilayer perceptron (MP) methods to improve prediction accuracy and extend water-level lead-time prediction. For the first time, the additive season algorithm (ASA) was used as an alternative data preprocessing technique for predicting water levels, and its performance was compared with that of wavelet transform (WT). The prediction accuracy and longer lead-time predictions were improved by the hybrid additive season algorithm-multilayer perceptron (ASA-MP) model. The results indicated that the hybrid additive season algorithm-multilayer perceptron model can be used to predict monthly water levels accurately with up to a 12-month lead time. The combined wavelet-multilayer perceptron (W-MP) model can be used to predict monthly water levels up to 6 months in advance with good agreement, whereas the standalone multilayer perceptron (MP) model can be used to predict the water levels up to 2 months in advance. The combined additive season algorithm-multilayer perceptron model outperformed the W-MP and standalone MP models based on the mean squared error (MSE) and the Nash-Sutcliffe coefficient of efficiency (CE) as performance evaluation criteria. As opposed to the ASA, the WT method has serious drawbacks and complicated mathematical processes. Furthermore, the prediction performance of the W-MP model was not satisfactory. The results of this study indicated that the ASA-MP model outperformed the W-MP model at all lead times. This implies that the season algorithm can effectively eliminate periodicity and trend cycles from original data better than the wavelet transform. In addition, the MP model should be combined with a preprocessing technique for more-accurate and longer lead-time predictions.

The existing classical methods for estimating the inelastic displacement ratios of reinforced concrete (RC) structures subjected to seismic excitation are built upon several assumptions that ignore the effect of uncertainties on the concerning phenomenon. Uncertainty techniques are more appropriate to modeling such phenomenon that inherits impreciseness. This research presents a new method predicting the inelastic displacement ratio of moderately degrading RC structures subjected to earthquake loading using expert systems such as fuzzy logic approach.

A well-defined degrading model was used to conduct the dynamic analyses. A total of 300 earthquake motions recorded on firm sites, including recent ones from Japan and New Zealand, with magnitudes greater than 5 and peak ground acceleration (PGA) values greater than 0.08 g, were selected. These earthquake records were applied on five RC columns that were chosen among 255 tested columns based on their beam-column element parameters reported by the Pacific Earthquake Engineering Research Center (2003) [1]. A total of 96,000 dynamic analyses were conducted. The results from these analyses were used to develop the fuzzy inelastic displacement ratio model inheriting uncertainties in terms of strength reduction factor (R) and period of vibration (T). The performance evaluation of the new fuzzy logic model and four classical methods were investigated using different independent data sets. As a result, more accurate results were predicted using the new fuzzy logic model. (C) 2014 Elsevier Ltd. All rights reserved.

Water wave is a powerful source of energy; the problem is harnessing and making use of it. Wave power is a renewable, pollution-free and sustainable energy source. Since wave power varies with the wave period and the square of significant wave height, it is necessary to quantify the variations and statistical characteristics of these parameters with time. The aim of this study was to derive a general wave power equation that can be used in order to determine the mean and the standard deviation of the wave parameters using perturbation approach. The result of the study showed that Weibull probability distribution function is able to accurately simulate wave power. The wave power equation developed in this study is a general formula and can be applied at any desired region. The equation was then applied to five stations and it was seen that the perturbation approach yields better statistical values compared to results determined using classical statistical approach. This indicates that it is necessary to use perturbation approach to achieve optimum wave turbine design. (C) 2014 Elsevier Ltd. All rights reserved.

Determining the contribution of variables from stations surrounding a pivot station to variables at the pivot station is very important for many purposes. In this regard, a Regional Dependency Function (RDF) among the variables needs to be obtained. RDF can be used to estimate missing data, determine the location and optimum number of measurement stations (station network design), estimate the potential of a variable under consideration and calculate radius of influence. However, conventional geostatistical methods cannot be employed to achieve the above mentioned uses as they have a number of limitations. As a result, a new method called Slope Point Cumulative Semi-Variogram (SPCSV), was developed to obtain RDF and to address all the limitations of the conventional geostatistical methods. SPCSV was developed by using data from 22 wave measurement stations located off the west coast of the United States. The objective of the study was to predict the significant wave height and determining the influence of radius of the pivot station using this method. Also, the SPCSV method was compared with two other geostatistical methods known as Point Cumulative Semi-Variogram (PCSV) and Trigonometric Point Cumulative Semi-Variogram (TPCSV) using the same data set by taking the mean relative error (MRE) as a performance evaluation criterion. The MRE of the SPCSV method was found to be 6%, which is acceptable in engineering applications. The superiority of the SPCSV method in predicting the significant wave height over the PCSV and TPCSV methods is presented both numerically and graphically. (C) 2015 Elsevier Ltd. All rights reserved.

Fuzzy logic techniques have been widely used in civil and earthquake engineering applications in the past four decades. However, no thorough research studies were conducted to use them for deriving attenuation relationships for peak ground accelerations (PGA). This paper is an attempt to fill this gap by employing a fuzzy approach with fuzzy sets for earthquake magnitude and distance from source with the objective of proposing new ground motion attenuation models. Recent earthquake records from USA and Taiwan with magnitudes 5 or greater were used; and consisted of horizontal peak ground acceleration recorded on three different site conditions: rock, soil and soft soil. The use of Fuzzy models to quantify ground motion records, which are typically characterized by a high level of uncertainty, leads to a rational analytical tool capable of predicting accurate results. Testing of the fuzzy model with an independent data set confirmed its accuracy in predicting PGA values. (C) 2014 Elsevier Ltd. All rights reserved.

Accurate daily rainfall prediction is required for accurate streamflow prediction, flooding risk analysis, constructing a reliable flood control and early warning system. However, because of its nonlinearity, prediction of daily rainfall with high accuracy and long prediction lead time is difficult. There are many daily rainfall prediction methods in the literature, but they are known to yield inaccurate predictions with short lead time, require many physical parameters and involve complicated mathematical equations with huge computational burden. Recently, artificial neural network has been used for predicting rainfall with the objective of addressing the above mentioned problems. But still, the accuracy has not been satisfactory and predictions are with short lead time. In this study, two methods called combined season-multilayer perceptron (SAS-MP) and hybrid wavelet-season-multilayer perceptron (W-SAS-MP) were developed to enhance prediction accuracy and extend prediction lead time of daily rainfall up to 5 days by using data from two stations in Turkey. These two models were compared with the stand-alone multilayer perceptron and another most commonly used method called combined wavelet-multilayer perceptron (W-MP). The performances of the models were evaluated by using coefficient of determination, coefficient of efficiency and root mean squared error. The SAS-MP model was found to be better than W-MP in most cases, except lead time day 1, where W-MP performed better. Throughout all the lead times, however, the hybrid W-SAS-MP model performed best with CE values of 0.911 and 0.909, respectively, for prediction lead time of 1 day and 0.588 and 0.570, respectively, for prediction lead time of 5 days at Stations 17836 and 17837, respectively, at the model testing (validation) phase. Therefore, W-SAS-MP can be an appropriate tool for enhancing daily rainfall prediction accuracy and extend prediction lead time. (C) 2015 Elsevier B.V. All rights reserved.

Neuro-fuzzy (NF) systems were applied to develop a new model for enhancing the prediction accuracy of breach formation time of embankment dams (t(f)), which is widely recognized to have uncertainty and affects the accuracy of dam breaks simulations. NF is based on expert knowledge which is trained by a learning algorithm derived from neural network theory and defined by a set of IF-THEN rules, each of which establishes a local linear input-output relationship between the variables of the model. The erodibility of the embankment material, the height of water and volume of water above the breach invert were taken as the input variables that contribute to t(f). Historical data from 45 embankment dam failures were randomly divided into two sets and used to train and test the performance of the developed model. Two models, without and with inclusion of the erodibility as input variable, were addressed. To train the models two different output types with ten different combinations of input fuzzy sets were examined. Therefore, 40 different NF models were investigated in order to obtain the best model that adequately resembles the observed t(f) values of the testing dataset. The results of the NF model were also compared with the results of the best available regression models (RM) and a recent gene expression programming (GEP). Three statistical evaluation parameters were used to evaluate the results of the models. Test results show the potential of the developed NF model which performed better than the available RM and GEP.

Accurate streamflow prediction is required in sustainable water resources management. Direct use of observed data in developing prediction models has resulted in inaccuracies. Discrete wavelet transform (DWT) is widely used to decompose observed data (raw data) into spectral bands and eliminate trends and periodicity to improve the accuracy of the models. However, DWT is known to have serious drawbacks, and predictions of daily streamflow have been with short lead times. In this study, a simple method called the SEASON algorithm was used to decompose the observed data into components with the objective of overcoming the drawbacks of DWT so that daily streamflow can be predicted with better accuracy and longer lead times. Data decomposed by SEASON and DWT were used as input into multilayer perceptron (MP) approaches to develop new approaches for predicting daily streamflow for lead times up to 7 days, and termed as seasonally adjusted series-multilayer perceptron (SAS-MP) and wavelet-multilayer perceptron (W-MP), respectively. Twelve years of approved daily streamflow data were obtained from Station 02231000 (located in the St. Marys River watershed) and Station 07288280 (located in the Big Sunflower River watershed), USA. Seven years of data were used for calibration (training) and the remaining 5 years of data were used for prediction (testing). The new approaches were compared with the stand-alone MP model by taking root mean squared error, coefficient of efficiency, and skill score into consideration. The results showed that the SAS-MP and W-MP models performed better than the stand-alone MP model, and the prediction accuracy increased with the use of decomposed signals. However, for all lead times, the SAS-MP model outperformed the W-MP model, which performed less after a lead time of 4 days. This indicates that the SEASON algorithm has the capability to capture periodicity better than DWT and can be used to extend lead time with better prediction reliability. (C) 2015 American Society of Civil Engineers.

Oceans cover more than 70% of the Earth's surface, and water waves are considered as unlimited sources of renewable energy. The use of fossil fuels may cause undesired challenges such as global warming and climate change in the nature. Advantages of renewable energy include low operational cost, environment friendliness, simple maintenance procedures, and non-polluting nature. In this study, an oscillating water column (OWC) system close to the onshore was investigated in water level for wave parameters which consist of different wave heights and wave periods. Efficient energy transformation is achieved by using air turbines. In this study, 20 experimental sets were carried out by a piston-type wave maker. The experimental results showed that the chamber geometry of the OWC, water depth, and wave parameters are most important factors in terms of achieving maximum wave power for energy harvesting.

Wavelet transforms are combined with predictive methods to develop prediction approaches so that the prediction accuracy can be improved in hydrologic predictions. Although the wavelet transform generates several subseries that show similar characteristics, the predictive method is used to develop the model using those subseries. There are several examples of these kinds of combined models, such as wavelet-multilayer perceptron (MP), wavelet fuzzy, wavelet autoregressive, and so forth. Generally, discrete wavelet transformation is used in combined models rather than continuous wavelet transform for unexplained reasons. As a result, in this study emphasis was placed on the comparison of the continuous wavelet-multilayer perceptron (CWT-MP) and discrete wavelet-multilayer perceptron (DWT-MP) models, which were also compared with the stand-alone MP model. Daily precipitation time series from two stations were used in the model development and comparison process. The current precipitation values were predicted from previous precipitation values. Various scenarios were used for the establishment of the models. Mean square error (MSE), coefficient of efficiency (CE), and score skill (SS) were used as model evaluation criteria, and it was observed that the prediction performance of MP was significantly improved by using wavelet transforms as preprocessing techniques. However, the CWT-MP models were found to be better than the DWT-MP models based on the results of the evaluation criteria.

This study investigated the effects of urbanization predicted using the SLEUTH urban growth model (an acronym taken from Slope, Landuse, Exclusion, Urban extent, Transportation and Hillshade) under four landuse policy scenarios on the hydrological response of Ayamama watershed using the Hydrologic Engineering Center Release 1 (HEC-1) hydrological model. The SLEUTH model was calibrated based on the Brute Force Monte Carlo iteration technique using the urban extents of Istanbul in 1987, 2000, 2009 and 2013 and was verified by considering Kappa coefficient as evaluation criteria. HEC-1 was calibrated and verified using observed rainfall-runoff event and based on the coefficient of determination (R-2), Nash-Sutcliffe coefficient of efficiency (CE) and percentage of bias (PBIAS) as performance indicators. The urbanization prediction results showed that the urban extent of Ayamama watershed would reach 50.3 km(2), 44 km(2), 63 km(2) and 60 km(2) under Scenarios 1, 2, 3 and 4, respectively, in 2050. The hydrological simulation results under these urban extents showed that the urban extent of Ayamama watershed under Scenario-3, a scenario that allows unrestricted growth with the implementation of Project Canal Istanbul (PCI), resulted in the highest peak discharge and the shortest time to peak. Such an increase in the peak discharge and reduction in the time to peak will increase the risk of flooding and, therefore, extreme care needs to be taken before and during the implementation of PCI.

Longitudinal dispersion coefficient can be determined by experimental procedures in natural streams. Many theoretical and empirical equations that are based on hydraulic and geometric characteristics have been developed from the field experiments of longitudinal dispersion coefficient. Regression analysis, which carries some restrictive, assumptions such as linearity, normality and homoscedasticity, was used to derive some of these equations. Generally speaking, results obtained from regression analyses are not that accurate as these assumptions are often not satisfied completely. In this study, a method called Prediction Map (PM) is developed based on geostatistics to predict longitudinal dispersion coefficient from measured discharge values, shear velocities, and other conventional parameters of the hydraulic variables and normalized velocity with the objective of overcoming the drawbacks indicated above. As part of this method, a new procedure called Iterative Error Training Procedure (IETP) was developed to minimize prediction error. The prediction error level was reduced after implementing the IETP PM was compared with various regression models by taking analyzed errors (average relative error percentage and root mean square error), coefficient of efficiency, coefficient of determination and Scatter Index as performance evaluation criteria. The results of the study indicate that the PM approach can perform very well in predicting longitudinal dispersion coefficient by applying IETP. The presented approach yielded lower average relative error percentage, root mean square error and Scatter Indices, and higher coefficient of efficiency and coefficient of determination values compared to the regression models. One of the important advantages of the PM method is that valuable interpretations and a prediction map can be extracted from the resulting contour maps, and as a result, more accurate predictions can be obtained compared to regression analysis. (C) 2016 International Association for Hydro-environment Engineering and Research, Asia Pacific Division. Published by Elsevier B.V. All rights reserved.

This study investigated the extent of urbanization that might result from the implementation of a project called Project Canal Istanbul (PCI) using the SLEUTH urban growth model and assessed its impact on the hydrological response of Ayamama watershed using the HEC-1 hydrological modelling tool. SLEUTH was run under two scenarios: Current scenario (without PCI) and PCI Scenario (with the implementation of PCI). The prediction results showed that the urban extent of Ayamama watershed would reach 44 km(2) and 63 km(2) under the current and PCI scenarios, respectively, by 2050. The HEC-1 simulation results under the indicated urban extents and various rainfall amounts showed that the urban extent of the watershed under the PCI scenario would resulted in a significant increase in the peak discharge. Such a change may increase the risk of flooding and, therefore, appropriate actions that can reduce such a risk need to be considered before the implementation of PCI.

There is a considerable amount of wave energy that can be extracted from the oceans. This energy is largely untapped. Oscillating Water Column (OWC) is a mechanical system that utilizes fluctuating water level from sea waves to drive an air turbine which, in turn, provides electricity when transmitted to a generator. In this study, two sets of modeling, each involving a numerical modeling and a physical experimental modeling, were conducted in a wave flume to optimize OWC systems. By varying the length, width and angle of the air chamber, an OWC structure can be designed to obtain the maximum system power. The data used for designing the optimal geometry of the chamber that may yield the maximum conversion of wave energy to useful energy were provided from the interpretation of the measurements of these parameters. In this study, results of the numerical models were compared with the measured experimental values based on the Nash-Sutcliffe coefficient of efficiency (NSE) as performance evaluation criterion. The NSE values of both the classical and the modified OWC structures were obtained to be 0.97. It is observed that the results of the numerical models tend to follow much closer the results of the experimental model.

Accurate prediction of water demand is essential for optimum management of water resources and sustainable growth and development. Recently, models based on artificial neural networks (ANNs), in combination with data preprocessing techniques, have been used for water demand prediction due to their ability to handle large amounts of complex nonlinear data. Discrete wavelet transform (DWT) is one of the most widely employed data preprocessing techniques, and is used in combination with ANNs to improve prediction accuracy and extend prediction lead time. However, DWT is known to have serious drawbacks, and the accuracy and prediction lead times of the models have not been satisfactory. In this study, multiplicative season algorithm (MSA) is applied for the first time as an alternative data preprocessing technique in the area of hydrology and its performance is compared with DWT. The outputs of MSA and DWT are used as inputs to a multilayer perceptron (MLP) in order to develop combined models called discrete wavelet transform-multilayer perceptron (DWT-MLP) and multiplicative season algorithm-multilayer perceptron (MSA-MLP), which are compared with the stand-alone MLP model. The results demonstrate that MSA is a better preprocessing technique than DWT and, thus, that MSA captures periodicity and converts nonstationary time series into stationary time series better than DWT. (C) 2017 American Society of Civil Engineers.

Urbanization competes with other important land uses such as agriculture and forests and makes ecosystem services and biodiversity sustenance a difficult challenge. It can also induce higher runoff peaks, increase the risk of flooding, and affect water quality. Informed planning and decision making, based on clear understanding of the extent of urbanization over time, can reduce these challenges. This can be achieved by investigating the effects of various scenarios reflecting various land-use policies. In this study, four scenarios were developed and their effects on the urban extent of Istanbul by 2050 were investigated by the slope, land use, exclusion, urban extent, transportation, and hillshade (SLEUTH) urban growth model. For this purpose, historical satellite images were used to develop all the input data using appropriate mapping software. The cell-by-cell matching index, which was found to be 0.773, was used as the performance evaluation criterion of the model. The result showed that the model can be used to predict the urban extent of Istanbul under the various scenarios. Accordingly, the total urban extent of Istanbul is predicted to reach 1,962, 1,188, 1,404, and 1,083 km(2) under Scenarios 1, 2, 3, and 4, respectively. Based on these results, urbanization under Scenario 1 could be taken as the worst scenario because it affects agricultural and forest land uses. As opposed to this, Scenario 4 is the most desirable for the future urbanization of Istanbul because it limits development in ecologically sensitive areas. However, with the implementation of Project Canal Istanbul, it also results in further urbanization within Ayamama and Tavukcu watersheds. Therefore, studying the potential impacts of urbanization under such scenarios in advance on the rainfall-runoff process and the environment as a whole in these areas is recommended so that appropriate actions could be taken to reduce associated risks. (C) 2016 American Society of Civil Engineers.

The water level of Urmia Lake, the largest inland lake in Iran with maximum water surface area of about 6000 km(2), has been shrinking for the last two decades. Although a number of study have been performed to determine drought condition and coastline changes of Urmia Lake, there has not been a detailed study to distinguish anthropogenic effects from climate impacts on the drying of Urmia Lake. In this study, water budget of Urmia Lake and the intensity of drought in the basin were analyzed in the period from 1985 to 2010 and a new hypothesis is proposed to quantify anthropogenic and climate impacts in reducing the volume of Urmia Lake. The results of this study indicate that human impacts on the Lake and its basin are more important than climate factors. Though previous studies assumed that ground water output from Urmia Lake is negligible, the results of this study show the presence of significant groundwater seepage from Urmia Lake. Major changes in the variables that reduced the water level of Urmia Lake were observed since 1998. Anthropogenic impacts and climate factors have roughly 80% and 20% effects on the drying up of Urmia Lake, respectively. Hence, the first step to recover Urmia Lake could be the revision of management surface water, operation of dams and groundwater resources. The second step could be the review and classification of agricultural products grown in the region in terms of water consumption and teach local people the best practice methods for irrigation.

In this study, combined Discrete Wavelet Transform-Multilayer Perceptron (DWT-MP), combined First-Order Differencing-Multilayer Perceptron (FOD-MP) and combined Linear Detrending-Multilayer Perceptron (LD-MP) were developed and compared with stand-alone Multilayer Perceptron (MP) model for predicting monthly water consumption of Istanbul. The performance of these models were assessed by using coefficient of determination (R-2), root mean square error (RMSE) and the Nash-Sutcliffe coefficient of efficiency (CE) as evaluation criteria. The study showed that DWT-MP could be used for forecasting the monthly water demand of Istanbul for only up to prediction lead-time of 3 months. However, FOD-MP was found to perform very well up to 12months. It can be concluded from the results of the study that First-Order Differencing (FOD) is a reliable pre-processing technique for monthly water demand prediction.

One of the most promising wave energy converter type is Oscillating Water Column (OWC) system. Fluctuation amounts and the motion behavior of the water column inside the chamber are very important parameters effecting the energy extraction. Therefore, predicting these parameters with respect to varying wave characteristics and geometric design parameters is of great importance. In this study, physical experiments are conducted for a bottom-fixed OWC system with seven different sizes of opening heights under various regular wave series. Average fluctuations inside the chamber are measured. It is found that there is a critical relative opening height ratio (alpha) that makes the fluctuations maximum regardless of wave parameters used. Exponential and linear relationships are found between average fluctuations and dimensionless parameters 'dimensionless wave frequency' and 'captured wavelength' defined, respectively. Mathematical models are developed to predict water column fluctuations under varying relative opening heights and wave parameters. The results of mathematical models indicated good aggrement with experimental data. Also chamber water surface profiles are observed and related to defined dimensionless wave parameters. Another factor (named as excessive harmful energy) is determined which also induces sloshing motion inside the chamber after the critical ratio value cc is exceeded. It is found that under all wave series, the highest relative average column water surface fluctuations occur at relative opening height a is equal to 0.67 which is a unique value. It can be concluded that mathematical models can be used to estimate water column fluctuations from relative opening height and wave parameter data in the chamber.

Sea wave power is one of the cleanest renewable energy resources with the potential to mitigate the challenges of global warming and climate change while contributing to the ever-increasing energy demand. Studies show that wave energy production is closely related to wave height and wave period. Accordingly, the potential assessment and characterization of wave energy is vital for planning, production and utilization of wave energy. This study investigated the monthly, seasonal and annual wave energy characteristics of the Black Sea using the third-generation, state-of-the-art numerical model, MIKE 21 SW, based on 37years of wind data obtained from the European Centre for Medium-Range Weather Forecasts. To set up the model and represent actual field conditions, computational mesh of the study domain was optimized and then the model was calibrated using data observed at nine points. According to the results of the study, the maximum mean monthly wave energy was obtained in the months of January and February. In terms of seasons, the maximum mean seasonal wave energy was observed in the winter. The analysis of the results at annual scale showed that the western part of the sea has more wave energy potential than the eastern part.

In this study, extreme rainfall indices of Olimpiyat Station were determined from reference period (1971-2000) and future period (2070-2099) daily rainfall data projected using the HadGEM2-ES and GFDL-ESM2M global circulation models (GCMs) and downscaled by the RegCM4.3.4 regional model under the Representative Concentration Pathway RCP4.5 and RCP8.5 scenarios. The Mann-Kendall (MK) trend statistics was used to detect trends in the indices of each group, and the nonparametric Wilcoxon signed ranks test was employed to identify the presence of differences among the values of the rainfall indices of the three groups. Moreover, the peaks-over-threshold (POT) method was used to undertake frequency analysis and estimate the maximum 24-h rainfall values of various return periods. The results of the M-K-based trend analyses showed that there are insignificant increasing trends in most of the extreme rainfall indices. However, based on the Wilcoxon signed ranks test, the values of the extreme rainfall indices determined for the future period, particularly under RCP8.5, were found to be significantly different from the corresponding values determined for the reference period. The maximum 24-h rainfall amounts of the 50-year return period of the future period under RCP4.5 of the HadGEM2-ES and GFDL-ESM2M GCMs were found to be larger (by 5.85%) than the corresponding value of the reference period by 5.85 and 21.43%, respectively. The results also showed that the maximum 24-h rainfall amount under RCP8.5 of both the HadGEM2-ES and GFDL-ESM2M GCMs was found to be greater (34.33 and 12.18%, respectively, for the 50-year return period) than the reference period values. This may increase the risk of flooding in Ayamama Watershed, and thus, studying the effects of the predicted amount of rainfall under the RCP8.5 scenario on the flooding risk of Ayamama Watershed and devising management strategies are recommended to enhance the design and implementation of adaptation measures.

Marmara Sea, located between Black Sea and Aegean Sea, is an important sea for ocean engineering activities. In this study, wave power potential of Marmara Sea was investigated using the third generation spectral wind-wave model MIKE 21 SW with unstructured mesh. Wind data was obtained from ECMWF ERA-Interim re-analyses wind dataset at 10 m with a spatial resolution of 0.1 degrees for the period of 1994 to 2014. The numerical model was calibrated with measured wave data from a buoy station located in Marmara Sea. Mesh optimization was also performed to obtain the most suitable mesh structure for the study area. This study is the first that dealt with the determination of wave energy potential of Marmara Sea. The numerical model results are presented in terms of monthly, seasonal and annual average of wave power flux (kW m(-1)). The maximum wave power flux is 1.13 kW m(-1) and occurs in November. The overall annual mean wave power flux during 1994-2014 is found to be 0.27 kW m(-1) in the offshore regions.

The impacts of climate change on extreme precipitation events in the Western Black Sea Basin of Turkey were investigated by using the annual maxima (AM) and peaks over threshold (POT) methods. Daily precipitation data measured between 1971 and 2000 at nine meteorological stations and projected and dynamically downscaled precipitation data from the outputs of GFDL-ESM2M, HadGEM2-ES and MPI-ESM-MR global circulation models (GCMs) under Representative Concentration Pathway 4.5 (RCP4.5) and RCP8.5 scenarios were used to generate maximum daily rainfall intensity for 2, 5, 10, 20, 50, 100 and 500 year return periods. The outputs of the GCMs were corrected by a modified linear scaling bias correction method to offset the uncertainties of the GCMs before frequency analyses. The datasets were fitted to a generalized extreme value distribution and a generalized Pareto distribution for the AM and POT methods, respectively. Based on a statistical test a reliable goodness-of-fit was obtained. The results of frequency analyses of daily storms by using the GCM data, after bias correction, showed that by the end of the current century the magnitude of the storms will increase by 31.29% and 27.00% based on the AM and POT methods respectively under the RCP4.5 scenario and by 43.51% and 31.29% based on the AM and POT methods respectively under the RCP8.5 scenario. The magnitude of the 24 hr rainfall intensity calculated by the AM method was more than that of the POT method by 22% on average. For the whole basin (on average) the mean maximum daily rainfall intensity was found to be 33.45, 46.18, 57.24, 69.16, 87.68, 104.51 and 154.60 mm/day for the respective return periods.

Predicting water level fluctuations in a lake is crucial in terms of sustainable water supply planning, flood control, management of water resources, shoreline maintenance, sustainability of ecosystem, and economic development. This study developed a new predictive model based on the season algorithm (SA) and multilayer perceptron (MP) methods to improve prediction accuracy and extend water-level lead-time prediction. For the first time, the additive season algorithm (ASA) was used as an alternative data preprocessing technique for predicting water levels, and its performance was compared with that of wavelet transform (WT). The prediction accuracy and longer lead-time predictions were improved by the hybrid additive season algorithm-multilayer perceptron (ASA-MP) model. The results indicated that the hybrid additive season algorithm-multilayer perceptron model can be used to predict monthly water levels accurately with up to a 12-month lead time. The combined wavelet-multilayer perceptron (W-MP) model can be used to predict monthly water levels up to 6 months in advance with good agreement, whereas the standalone multilayer perceptron (MP) model can be used to predict the water levels up to 2 months in advance. The combined additive season algorithm-multilayer perceptron model outperformed the W-MP and standalone MP models based on the mean squared error (MSE) and the Nash-Sutcliffe coefficient of efficiency (CE) as performance evaluation criteria. As opposed to the ASA, the WT method has serious drawbacks and complicated mathematical processes. Furthermore, the prediction performance of the W-MP model was not satisfactory. The results of this study indicated that the ASA-MP model outperformed the W-MP model at all lead times. This implies that the season algorithm can effectively eliminate periodicity and trend cycles from original data better than the wavelet transform. In addition, the MP model should be combined with a preprocessing technique for more-accurate and longer lead-time predictions.

In this study, combined Discrete Wavelet Transform-Fuzzy (DWT-Fuzzy) and combined Continuous Wavelet Transform-Fuzzy (CWT-Fuzzy) models are developed for predicting the daily water levels at northern and southern boundary of Bosphorus Strait. The observed daily water level data is decomposed into spectral bands (sub-series) by using wavelet transformation as a pre-processing tool in order to achieve more accurate daily water level predictions with extended lead-times up to 7 days. The time series of daily water level data is decomposed into spectral bands, which are used as inputs into the Fuzzy model and the daily water levels are predicted from the sum of the predicted components (spectral bands). A predictive model is developed using combined DWT-Fuzzy and combined CWT-Fuzzy models to predict water level fluctuations. It is found that CWT-Fuzzy model performed better than DWT-Fuzzy and stand-alone Fuzzy models for prediction lead-times up to 7 days at northern and southern boundary of Bosphorus based on RMSE and CE evaluation criteria. It is concluded that CWT is a better pre-processing technique as it yields more accurate daily water level predictions with improved prediction lead-times than DWT.

In this present study, a novel approach is introduced for accurate prediction of the incipient motion of uniform grain particles in sand and gravel-bedded open channels under unidirectional flow by improving Sugeno Fuzzy Inference System (Sugeno FIS). The Adaptive Neural Fuzzy Inference System (ANFIS) tool is based on Sugeno FIS. Consequent part of the ANFIS tool is limited to either as a constant or a linear function. This means that not only a non-linear function is available for the consequent part of the model but also it cannot be represented by the constant and linear functions, at the same time. ANFIS tool optimizes antecedent parameters (fuzzy sets) and consequent parameters (constant or linear functions) by utilizing neural and least square methods, respectively. In this study, a novel hybrid model named as Geno-Fuzzy Inference System (GENOFIS) is introduced by integrating improved Sugeno FIS and Genetic Algorithms (GAs) that refer to where, the antecedent (fuzzy sets) and consequent (constant, linear and non-linear functions) parameters are optimized using Genetic Algorithms (GAs) tool. A quantitative comparison is implemented between the ANFIS and GENOFIS models using root mean square error (RMSE) and the Nash-Sutcliffe coefficient of efficiency (CE) as evaluation criteria by using three different types of incipient motion of sediment measurements that exist in the literature as reference, visual and development of competence functions. The results of this present study demonstrated that the novel GENOFIS model provided more accurate prediction results in comparisons with the ANFIS model results for three different types of incipient motion of sediment.