Estimation of Infiltration Rate using a Nonlinear Regression Model

Abstract

The primary abstraction of precipitation is infiltration. Accurate estimation of the infiltration rate is helpful in determining the direct runoff, groundwater recharge, water availability, and degree of saturation of soil. In this study, infiltration rate was estimated for the soils in Vishakhapatnam, Andhra Pradesh, India. The agriculture sector in the study region contributes to about 12% of the total GDP of the state. The study region faces an acute shortage of water and hence, accurate estimation of infiltration rates is vital. In this regard, the application of an Excel Solver was explored in estimating the infiltration rates. The results of the infiltration rates obtained through the Excel Solver were compared with the conventional Graphical method. In the present study, the best infiltration model was selected using the sum of square error (SSE), Maximum Absolute Error (MAE), Wilmot Index (WI), Mean Absolute Relative Error (MARE), and Nash-Sutcliffe Efficiency (NSE). Horton’s model was found to be the best-fit model for the Samantha Hostel, NCC Building, Assembly Hall, and CSE building. For the dispensary, the Kostiakov model was found to be the best-fit model of the study area. Upon application of the Excel Solver, the SSE was reduced by 70.48%, 39.55%, and 87.39% for the Kostiakov, Horton, and SCS models, respectively, as compared to the Graphical method. The WI improved by 5.68%, 36.39%, and 17.85% for the Kostiakov, Horton and SCS models, respectively, for the Excel Solver model as compared to the Graphical method. Based on this analysis, the Excel Solver was found to be a reliable tool for determining the parameters of nonlinear equations, and therefore its application should be explored in different fields of engineering.