Prediction of groundwater level in Birjand Plain using the Fuzzy Artificial Neural network under the effects of climate change

Nowadays؛ climate change and its effects creates a major crisis on the management of water resources. On the other hand؛ since groundwater is one of the most important sources of water supply؛ analyzing the impact of climate change on groundwater in the upcoming period will be necessary. The goal of current study is investigation of the effects of climate change scenarios for the balance of the groundwater level؛ case study on the subterranean water level in the field of Birjand South Khorasan province. In line with this goal in order to evaluate the effects of climate change؛ 26 parameters from the output of the Canadian General Circulation Models (CGCM3.1 AR5) under the emission scenario RCP4.5 was used as predictors. As well as؛ the SDSM model was applied for downscaling the climate data to generate the regional daily scenarios. The Artificial neural Network Fuzzy Integrated System (ANFIS) was used to investigate the changes in the balance of underground water. The results o؛ optimal and intelligent management of groundwater resources and improving the consumption pattern seems necessary.