Over the past decade, artificial neural networks (ANN) have been widely used in the runoff modeling studies. In spite of a number of advantages, ANN models have some drawbacks, including the possibility of getting trapped in local minima, over training, subjectivity in the determining of model parameters, initialization of the weights in each simulation randomly, and the components of its complex structure. In the past decade, a new alternative kernel‐based technique called a support vector machine (SVM) has been found to be popular in modeling studies because of its advantages over ANN. Least squares version of support vector machines (LS‐SVM) provides a computational advantage over standard support vector machines by converting quadratic optimization problem into a system of linear equations. The LS‐SVM method is preferred in this study. The main purposes of this study are to examine the applicability and capability of LS‐SVM for the prediction of runoff values of Tahtali and Gordes watersheds, which are the major surface water resources for the city of Izmir in Turkey, and to compare its performance with ANN and other traditional techniques such as autoregressive moving average and multiple linear regression models. For these purposes, meteorological data (rainfall and temperature) and lagged data of runoff were used in modeling applications. Some favorite statistical performance evaluation measures were used to assess models. The results in study indicate that the LS‐SVM and ANN methods are successful tools to model the monthly runoff series of two study regions and can give better prediction performances than conventional statistical models. Although these two methods are powerful artificial intelligence techniques, LS‐SVM makes the running time considerably faster with the same or higher accuracy. In terms of accuracy, the LS‐SVM models, which involve different normalization types, resulted in increased accuracy to that of the ANN models. Copyright © 2012 John Wiley & Sons, Ltd.

In the study presented, different hybrid model approaches are proposed for reservoir inflow modeling from the meteorological data (monthly precipitation, one-month-ahead precipitation and monthly mean temperature data) by the combined use of discrete wavelet transform (DWT) and different black box techniques. Multiple linear regression (MLR), feed forward neural networks (FFNN) and least square support vector machines (LSSVM) were considered as the black box methods. In the modeling strategy, meteorological input data were decomposed into wavelet sub-time series at three resolution levels and ineffective sub-time series were eliminated by Mallows' C p based all possible regression method. As a result of all possible regression analyses, 2-months mode of time series of monthly temperature (D1_T t ), 8-months mode of time series (D3_T t ) of monthly temperature and approximation mode of time series (A3_T t ) of monthly temperature were eliminated. Remained effective sub-time series were used as the inputs of MLR, FFNN and LSSVM. When the performances of the training and testing periods were compared, it was observed that the DWT-FFNN conjunction model has better results in terms of mean square errors (MSE) and determination coefficients (R 2 ) statistics. The discrete wavelet transform approach also increased the accuracy of multiple linear regression and least squares support vector machines.

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Considering the effects of rapid population growth, urbanisation and climate change in recent years, the protection of freshwater resources, the prevention of water pollution and the proper sharing of freshwater resources among different sectors have become important issues. Water footprint (WF) is a sign of freshwater use and is not only an indicator that can be used in the climate crisis, but also to protect water against nitrate pollution. In this study, the Küçük Menderes Basin was chosen as the study area due to different crop varieties. Agricultural crop patterns were classified using Rapideye and Sentinel-2 satellite images of the study area obtained in 2017. Thus, the cultivated areas were obtained for cotton and maize (grain and silage) in the basin. In particular, agricultural crop patterns were considered in which agricultural production was intensive and blue water was used predominantly. As a result, the first-crop corn production, which has a high blue WF of 3840 m3/ton in the basin, has the highest greywater footprint due to the use of intensive chemical fertilisers. This was followed by cotton with 2331 m3/ton, and the second-crop silage corn production had the lowest greywater footprint. Agriculture’s water footprint assessment provides a solid foundation for planning climate change adaptive crop production, managing nitrate-sensitive areas and anticipating future regional changes.