Simulation of rainfall-runoff process using geomorphology-based adaptive neuro-fuzzy inference system (ANFIS)

This research was conducted to present an integrated rainfall-runoff model based on the physical characteristics of the watershed, and to predict discharge not only in the outlet, but also at any desired point within the basin. To achieve this goal, a matrix of hydro-climatic variables (i.e. daily rainfall and daily discharge) and geomorphologic characteristics such as upstream drainage area (A), mean slope of watershed (S) and curve number (CN) was designed and simulated using artificial intelligence techniques. Integrated Geomorphology-based Artificial Neural Network (IGANN) model with Root Mean Squared Error (RMSE) of ۰.۰۲۷۸۶ m۳ s-۱ and Nash-Sutcliffe Efficiency (NSE) of ۰.۹۴۰۳ and Integrated Geomorphology-based Adaptive Neuro-Fuzzy Inference System (IGANFIS) model with RMSE of ۰.۰۲۷۹۵ m۳ s-۱ and NSE of ۰.۹۴۴۶۷ were able to predict the discharge values of all hydrometric stations of the Chalus River watershed with a very low error and high accuracy. The results of cross validation stage confirmed the efficiency of models. Hydro-climatic variables and geomorphologic parameters selected in the study were: discharge of one day ago, discharge of two days ago, rainfall of current day and rainfall of one day ago and S, CN and A, respectively. In addition, the IGANN model shows superiority compared with the IGANFIS model.

Keywords:

Physical characteristics of watershed , Rainfall-runoff modeling , Black box modeling , Artificial intelligence , Geomorphologic unit hydrograph