Comparison of Rainfall-Runoff Relationship Modeling using Different Methods in a Forested Watershed

Abstract

The daily rainfall-runoff relationship in an experimental watershed was modeled using a statistical method and an artificial neural network method. The estimations were examined and a performance evaluation was done. It was seen that the ANN method, FFBP (Feed Forward Back Propagation), provided closer flow estimations reproducing the shape of the observed hydrograph more realistic. The superiority of FFBP was reflected in the performance evaluation criteria. The extreme flows, i.e., high and low flows, were relatively better approximated by FFBP indicating its promise as a useful tool for hydrologic studies such as flood modeling. The Rational Method was also used, as a conventional tool, to predict the maximum discharge for selected return periods. It was found to be realistic for the forested watershed under consideration when the C coefficient was taken as 0.20 for the 10-year period.