RBFNN Versus Empirical Models for Lag Time Predict

Authors: Mohammed Seyam Faridah Othman Ahmed ElShafie

Publish Date: 2016/10/25

Volume: 31, Issue: 1, Pages: 187-204

PDF Link

Abstract

Lag time Lt reflects the speed at which a river basin responds to rainfall RF events and is influenced by many hydrological parameters such as RF and stream flow SF These two parameters are represented by four variables namely peak RF intensity previous 48h rainfall peak SF and previous 48h SF In fact lag time is highly stochastic in nature and its relation with the mentioned four variables is highly nonlinear interrelationship The main objective of this study is to develop a model to estimate the Lt between upstream and downstream stations in tropical humid rivers The graphical hydrological approach HGA has been used to estimate the Lt based on 95 RFSF and considered as the references value for the proposed model evaluation Linear nonlinear and Radial Basis Function Neural Network RBFNN methods have been developed successfully for Selangor River basin The results show that the RBFNN outperformed the linear and the nonlinear model and could achieve correlation coefficient r between the observed Lt and predicted Lt equal to 0979 while r for the linear and the nonlinear model equal to 0519 and 0631 receptively Furthermore the RBFNN model could attain minimum root mean square error RMSE between the observed Lt and predicted Lt equal to 123 while RMSE for the linear and the nonlinear model equal to 19 and 202 receptively The proposed RBFNN model significantly abridges the estimation of Lt values and avoids the essential need for comprehensive description of all parameters affecting on its valueThe authors would like to acknowledge the support rendered by the Hydrology and Water Resources Division of the Department of Irrigation and Drainage DID and other relevant agencies We are also very thankful to the University of Malaya for providing the financial assistance through University Malaya Research Grant FL0262012D FL00113SUS and RP017C15SUS We are most grateful and would like to thank the reviewers for their valuable suggestions that have led to substantial improvements to the article

Keywords: