Neural networks and M5 model trees in modelling water level-discharge relationship

Authors:
B. Bhattacharya;D. P. Solomatine
Affiliations:
Department of Hydroinformatics and Knowledge Management, UNESCO-IHE Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands;Department of Hydroinformatics and Knowledge Management, UNESCO-IHE Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands
Venue:
Neurocomputing
Year:
2005

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Abstract

Reliable estimation of discharge in a river is the crucial component of efficient flood management and surface water planning. Hydrologists use historical data to establish a relationship between water level and discharge, which is known as a rating curve. Once a relationship is established it can be used for predicting discharge from future measurements of water level only. Successful applications of machine learning in water management inspired the exploration of applicability of these approaches in modelling this complex relationship. In the present paper, models of the water level-discharge relationship are built with an artificial neural network (ANN) and an M5 model tree. The relevant inputs are selected by computing average mutual information. The predictive accuracy of this model is compared with a traditional rating curve built with the same data. It is concluded that the ANN- and M5 model tree-based models are superior in accuracy than the traditional model.