Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China

Hydrology and Earth System Sciences - Tập 6 Số 4 - Trang 619-626
Christian W. Dawson1, Colin Harpham2, Robert L. Wilby3, Y. Chen4
1Department of Computer Science, Loughborough University, Leicestershire LE11 3TU, UK
2School of Computing and Technology, University of Derby, Kedleston Road, Derby DE22 1GB, UK
3Department of Geography, King's College London, Strand, London WC2R 2LS,#N#UK
4Institute of Hydrology and Water Resources, Three Gorges University, Yichang, Hubei, China

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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. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting

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Hydrology and Earth System Sciences

Tập 6 Số 4

619-626

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