Katipoglu, Okan Mert

This study aimed to predict monthly flows using an adaptive neuro-fuzz y inference system (ANFIS) and wavelet-ANFIS (W-ANFIS) and to deter-mine the effect of wavelet transformation on the success of the machine learning model. For this purpose, the model inputs are divided into three subcomponents with Daubechies 10 mother wavelets. Subcomponents with the highest correlation were chosen as inputs. The most suitable models were selected by dividing the inputs into 3-7 sub-sets, using 11 different lagged input combinations, and testing various membership functions. In establishing the ANFIS model, 75% of the data were used for training and 25% for testing. The performance of ANFIS models was evaluated with root mean square error, Pearson correlation coefficient, determination coefficients, and Taylor diagram. A model with two sub-sets, a hybrid learning algorithm, a Gbellmf membership function, and 400 iterations was selected as the most suit-able. It was concluded that the W-ANFIS model used with the wavelet transform method increased the success of the established ANFIS model. Moreover, it was suggested that the W-ANFIS hybrid machine learning model established in the study can be used effectively in simi-lar climatic regions fed by snowmelt and dominated by a semi-arid climate.

KatipoğLu, Okan Mert; Sarıgöl, Metin

Flood routing is vital in helping to reduce the impact of floods on people and communities by allowing timely and appropriate responses. In this study, the empirical mode decomposition (EMD) signal decomposition technique is combined with cascade forward backpropagation neural network (CFBNN) and feed-forward backpropagation neural network (FFBNN) machine learning (ML) techniques to model 2014 floods in Ankara, Mera River. The data are split in order to avoid the underfitting and overfitting problems of the algorithm. While establishing the algorithm, 70% of the data were divided into training, 15% testing and 15% validation. Graphical indicators and statistical parameters were used for the analysis of model performance. As a result, the EMD signal decomposition technique has been found to improve the performance of ML models. In addition, the EMD-FFBNN hybrid model showed the most accurate estimation results in the flood routing calculation. The study’s outputs can assist in designing flood control structures such as levees and dams to help reduce flood risk.

Katipoglu, Okan Mert; Acar, Reşat; Şenocak, Serkan

In this study, the aim was to measure changes in the spatio-temporal distribution of a potential drought hazard area and determine the risk status of various meteorological and hydrological droughts by using the kriging, radial basis function (RBF), and inverse distance weighting (IDW) interpolation methods. With that goal, in monthly, three-month, and 12-month time periods drought indices were calculated. Spatio-temporal distributions of the droughts were determined with each drought index for the years in which the most severe droughts were experienced. According to the results, the basin is under risk of meteorological drought due to the occurrence of severe and extreme droughts in most of the area, and especially in the north, during the monthly and three-month time periods. During the 12-month period, it was found that most of the basin is under risk of hydrological drought due to the occurrence of severe and extreme droughts, especially in the southern parts. The most effective interpolation method for the prediction of meteorological and hydrological droughts was determined as kriging according to the results of the cross-validation test. It was concluded that a drought management plan should be made, and early warnings and precautions should be applied in the study area.

Dalkiliç, Hüseyin Yildirim; Hashimi, Said Ali

In recent years, the prediction of hydrological processes for the sustainable use of water resources has been a focus of research by scientists in the field of hydrology and water resources. Therefore, in this study, the prediction of daily streamflow using the artificial neural network (ANN), wavelet neural network (WNN) and adaptive neuro-fuzzy inference system (ANFIS) models were taken into account to develop the efficiency and accuracy of the models' performances, compare their results and explain their outcomes for future study or use in hydrological processes. To validate the performance of the models, 70% (1996-2007) of the data were used to train them and 30% (2008-2011) of the data were used to test them. The estimated results of the models were evaluated by the root mean square error (RMSE), determination coefficient (R2), Nash-Sutcliffe (NS), and RMSE-observation standard deviation ratio (RSR) evaluation indexes. Although the outcomes of the models were comparable, the WNN model with RMSE= 0.700, R2 = 0.971, NS = 0.927, and RSR = 0.270 demonstrated the best performance compared to the ANN and ANFIS models.