Evaluating the effect of defining management scenarios in water table prediction accuracy using Wavelet-Support Vector Regression (WSVR) hybrid model

Due to surface water limitations, groundwater reservoirs are known as the main source of supplying water requirements in most arid countries. Recently, owing to these aquifers’ over-exploitation, in order to apply optimal management of these resources, reliable water table forecasting exceeded in importance. This study is aimed to Determine the best scenario for combining input data in Hamedan-Bahar plain water table forecasting using the wavelet-support vector regression (WSVR) hybrid model. In the first step, Rainfall, temperature, evaporation, and the groundwater level data of seventeen piezometers in 26 years (1991-2017) were collected and completed. Nine scenarios with different lags and combinations were considered to select the model inputs and their lag numbers. Modeling performance in each scenario was evaluated using statistical parameters such as the Pearson correlation coefficient (r), standard error (SE), and root mean square error (RMSE), and using the best scenario, the water table of the area was predicted for ten years ahead. Based on the obtained results, the scenario in which each of the four input parameters was used, with one and two lags, had the highest accuracy. Additionally, the predicted results, using the best scenario, illustrated a noticeable downward trend in the water table of the region in the future. Therefore, concerning the high sensibility of this plain because of supplying the water demands in drinking water, agriculture, and industry of Hamedan and Bahar, as well as the necessity of more water harvesting in the future, deciding more favorable groundwater management is highly imperative in this area. کلیدواژه‌ها [English] Support vector machines Water table prediction Wavelet transport WSVR hybrid model