Ground Water Level Estimation for Dörtyol region in HATAY

Abstract

Accurate and reliable estimation of groundwater level is important for the development and management of water resources. In this study, models of adaptive neuro-fuzzy inference system (ANFIS) with multiple linear regression (MLR) method and its performance in predicting groundwater level were investigated. As a field of application, it was applied for General Directorate of State Hydraulic Works (DSİ) 5512 well of Dörtyol region of Hatay province. In the study, 147-month data sets between 2000 and 2015, including hydrological parameters such as Precipitation (P), average air temperature (T), relative humidity (RH), wind speed (W) groundwater level (GWL) time series, predict the groundwater level used. The determinant coefficient (R 2 ), mean square error (MSE) and mean absolute error (MAE) were used as the statistical performance evaluation criteria. As a result of this study, MLR and ANFIS models performed well for GWL estimation. In particular, the ANFIS model yielded better results than the MLR model.

Forecasting of River Sediment Amount using Machine Model

“…For these reasons, the estimation of sediment amount is needed in the design of water structures. In the last years, the artificial intelligence approaches are a technique widely used in water resources engineering and hydrology [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Thangaraj and Kalaivani [18] estimated the water level in the river using support vector machines.…”