Hydrological modelling using artificial neural networks

This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging fie ld of research, characterized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and traini ng algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardi zation techniques; and methods of evaluating ANN model performance. A liter ature survey underlines the need for clear guidance in current modelling pr actice, as well as the comparison of ANN methods with more conventional sta tistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggeste d that research might focus on the extraction of hydrological 'rules' from ANN weights, and on the development of standard performance measures that p enalize unnecessary model complexity.