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A data‐driven algorithm for constructing artificial neural network rainfall‐runoff models

Hydrological Processes · 2002 · Vol. 16(6) · pp. 1325–1330

K. P. Sudheer✉(National Institute of Hydrology)A. K. Gosain(Indian Institute of Technology Delhi)K. S. Ramasastri(National Institute of Hydrology)

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Abstract

Abstract A new approach for designing the network structure in an artificial neural network (ANN)‐based rainfall‐runoff model is presented. The method utilizes the statistical properties such as cross‐, auto‐ and partial‐auto‐correlation of the data series in identifying a unique input vector that best represents the process for the basin, and a standard algorithm for training. The methodology has been validated using the data for a river basin in India. The results of the study are highly promising and indicate that it could significantly reduce the effort and computational time required in developing an ANN model. Copyright © 2002 John Wiley & Sons, Ltd.

Hydrological Forecasting Using AI Hydrology and Drought Analysis Hydrology and Watershed Management StudiesArtificial neural networkSurface runoffComputer scienceAlgorithmSeries (stratigraphy)Process (computing)Data miningTime seriesHydrological modellingHydrology (agriculture)

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