Rainfall–runoff modeling using an Adaptive Neuro-Fuzzy Inference System considering soil moisture for the Damanganga basin

Abstract

Rainfall is the major component of the hydrologic cycle and it is the primary source of runoff. The main purpose of this study was to estimate daily discharge by employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model using rainfall and soil moisture data at three different depths (5 cm, 100 cm and bedrock) for the Damanganga basin. The length of the data for the study period 1983–2022 is 39 years. The model employed nine membership functions for each variable of soil moisture, rainfall, discharge and 30 rules were optimized. The results were compared considering a range of model performance indicators as correlation coefficient (R2) and Nash–Sutcliffe efficiency (NSE) coefficient. The model application results shows that soil moisture at bedrock gives more precise value of daily discharge with (R2) and NSE value as 0.9936 and 0.9981, respectively, as compared to the soil moisture at depths of 5 and 100 cm. The better results obtained for the measurement of soil moisture in the deeper soil layer are consistent with the hydrological behavior anticipated for the analyzed catchment, where the root-zone soil layer is the driver of the runoff response rather than the surface observations. This study can be helpful to hydrologists in selecting appropriate rainfall–runoff models. HIGHLIGHTS To estimate the daily discharge by employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model using rainfall and soil moisture data.; The model employed nine membership functions for each variable of soil moisture, rainfall, discharge and 30 rules were optimized.; The results were compared considering a range of model performance indicators such as correlation coefficient (R2) and Nash-Sutcliffe efficiency (NSE) coefficient.;

Keywords

• anfis
• discharge
• rainfall
• soil moisture