Artificial neural networks (ANNS) for daily rainfall runoff modelling

Abstract

Rainfall-runoff relationships are among the most complex hydrologic phenomena. Hydrologists have developed conceptual models to simulate runoff but these are composed of a large number of parameters and the interaction is highly complicated. ANN is an information-processing system composed of many nonlinear and densely interconnected neurons. ANN is able to extract the relation between the inputs and outputs of a process without the physics being provided to them. Natural behavior of hydrological processes is appropriate for the application of ANN in hydrology. Nowadays, ANNs are used to build rainfall-runoff models, estimate pier scour. Daily rainfall-runoff model for Sungai Bedup Basin, Sarawak was built using MLP, REC networks. Inputs used are antecedent rainfall, antecedent runoff and rainfall while output was the runoff. ANNs were trained using different training algorithms, learning rates, length of data and number of hidden neurons. All data was collected from DID Sarawak. Results were evaluated using Coefficient of Correlation (R) and Nash-Sutcliffe Coefficient (E2). Results show that ANNs is able to simulate daily runoff with high accuracy (R=0.97). REC performs slightly better than MLP.