An evaluation of artificial neural network techniques for flow forecasting in the river Yangtze, China

C. W. Dawson, C. Harpham, R. L. Wilby, Y. Chen

Research output: Contribution to journalArticlepeer-review

88 Citations (Scopus)

Abstract

While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP), and the radial basis function network (RBF). Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam) for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach.
Original languageEnglish
Pages (from-to)619-626
Number of pages8
JournalHydrology and Earth System Sciences
Volume6
Issue number4
DOIs
Publication statusPublished - 2002

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