Rainfall runoff modelling using neural networks: state-of-the-art and future research needs

Abstract

Modeling of rainfall runoff (R-R) processes is useful in many water resources management activities. Traditionally, hydrologists have employed deterministic/conceptual methods for R-R modeling. Recently, Artificial Neural Networks (ANNs) have become popular tools for R-R modeling. This paper reviews the literature on and presents state-of-the-art approaches to ANN R-R modeling. Certain aspects of ANN R-R modeling have been covered in greater detail. These include input selection, data division, ANN training, hybrid modeling, and extrapolation beyond the range of training data. There is a strong need to carry out extensive research on these aspects while developing ANN R-R models. © 2009 Taylor & Francis Group, LLC.