Open AccessPrediction of annual runoff using Artificial Neural Network and Regression approaches
Author(s) -
N. VIVEKANANDAN
Publication year - 2011
Publication title -
mausam
Language(s) - English
Resource type - Journals
ISSN - 0252-9416
DOI - 10.54302/mausam.v62i1.4711
Subject(s) - surface runoff , artificial neural network , mean squared error , regression , correlation coefficient , environmental science , predictive modelling , regression analysis , black box , runoff curve number , statistics , hydrology (agriculture) , computer science , meteorology , mathematics , machine learning , engineering , artificial intelligence , geography , ecology , geotechnical engineering , biology
Prediction of runoff is often important for optimal design of water storage and drainage works andmanagement of extreme events like floods and droughts. Rainfall-runoff (RR) models are considered to be most effectiveand expedient tool for runoff prediction. Number of models like stochastic, conceptual, deterministic, black-box, etc. iscommonly available for RR modelling. This paper details a study involving the use of Artificial Neural Network (ANN)and Regression (REG) approaches for prediction of runoff for Betwa and Chambal regions. Model performanceindicators such as model efficiency, correlation coefficient, root mean square error and root mean absolute error are usedto evaluate the performance of ANN and REG for runoff prediction. Statistical parameters are employed to find theaccuracy in prediction by ANN and REG for the data under study. The paper presents that ANN approach is found to besuitable for prediction of runoff for Betwa and Chambal regions.