Achite, Mohammed; Emami, Somayeh; Jehanzaib, Muhammad; Katipoğlu, Okan Mert; Emami, Hojjat

The prediction of hydrological drought is significant for the management of water resources, planning of hydroelectricity production, agricultural production, habitat, and life of living things. The primary aim of this study is to increase the prediction success hydrological drought in the Wadi Ouahrane basin (270 km2). For this purpose, support vector regression technique is hybridized with Election Algorithm. Standardized Runoff Index (SRI) values were used to determine hydrological droughts. In calculating droughts, rainfall and stream flow data covering the years 1972–2018 were employed. Standardized Precipitation Index (SPI) and previous SRI values were used in the establishment of the hydrological drought prediction model. The coefficient of determination, Standard deviation, the Akaike information criterion and root-mean-square error values were used to test the model accuracy. It was identified to be the most accurate according to the research outputs were obtained with SRI-12 and the Election Algorithm-based support vector regression algorithm showed successful results in hydrological drought prediction. In addition, it was revealed that realistic results are obtained when SPI value and delayed SRI values were used as inputs in multi-time scale analysis of hydrological drought.

Katipoğlu, Okan Mert

Due to climate change and increasing demand for water, effective planning of water resources is a current issue. Reliable and accurate streamflow prediction is of great importance in the planning of water resources. This study aimed to predict monthly streamflows in Amasya by combining a discrete wavelet transform and a feedforward backpropagation neural network (FFBPNN) model. Various meteorological variables were separated into sub signals with mother wavelets commonly used in hydrometeorological studies, such as Haar, Daubechies 2, Daubechies 4, Discrete Meyer, Coiflet 3, Coiflet 5, Symlet 3, and Symlet 5, and entered into the FFBBNN model to create a hybrid wavelet-based FFBBNN model. Inputs with a significant relationship with the output were entered into the model. Precipitation, temperature, and previous streamflow values covering 1960–2011 were used to create the model. During the modeling phase, 70% of the data were divided into training, 15% into validation, and 15% into testing. The performance of the model was compared using mean square error, correlation coefficient, and rank analysis. Coiflet 5 mother wavelet showed the best results. Moreover, it was proven that monthly streamflow can be successfully predicted using previous precipitation, temperature, and streamflow values and the Coiflet 5 mother wavelet with the FFBBNN hybrid model (MSE: 7.143, R: 0.921). In addition, all built wavelet FFBPNN models except the Symlet 3 mother wavelet performed better than the single FFBPNN model. The results of the study will assist planners, and decision makers in terms of providing sustainable and effective water resources and drought management.