Runoff Prediction Using Artificial Neural Network and SCS-CN Method: A Case Study of Mayurakshi River Catchment, India

Abstract

Runoff estimation, as well as forecasting, is a challenging hydro-climatological topic since governing physical processes is complex, and in reality, it is hardly represented by a system of the equations. Due to the complex nature and extreme spatial-temporal variability of the processes which control runoff, it is difficult to set up a reliable framework for runoff prediction and forecasting based on available observations only. In this research, two kinds of methods have been approached. The first one is a conceptual method, Soil Conservation Service Curve Number (SCS-CN) method, which combines the climatic factors and watershed parameters in one unit called the  Curve Number (CN). The other method is the Artificial Neural Network (ANN) modeling, where two different kinds of models, Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation (CFBP) model have been applied. The runoff-rainfall coefficient has been chosen as the standard parameter of the study result. Among 16 years, the year 2000 has the highest annual, seasonal monthly total runoff (monsoon season, July to Sept.). In artificial neural network models, generated coefficient correlation (R) values varied from 0.96 to 0.99 range, which indicated a good correlation between the rainfall-runoff data set. The models developed for the present study can be utilized for further basin hydrologic analysis.