淡江大學機構典藏:Item 987654321/44607
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    Title: Multi-step-ahead Neural Networks for Flood Forecasting
    Other Titles: Réseaux de neurones à échéances multiples pour la prévision de crue
    Authors: Chang, Fi-John;Chiang, Yen-ming;張麗秋;Chang, Li-chiu
    Contributors: 淡江大學水資源與環境工程學系
    Keywords: neural networks;multi-step-ahead;flood forecasting;serial-propagated structure;Taiwan
    Date: 2007-02-01
    Issue Date: 2010-08-10 11:25:31 (UTC+8)
    Publisher: Taylor & Francis
    Abstract: A reliable flood warning system depends on efficient and accurate forecasting technology. A systematic investigation of three common types of artificial neural networks (ANNs) for multi-step-ahead (MSA) flood forecasting is presented. The operating mechanisms and principles of the three types of MSA neural networks are explored: multi-input multi-output (MIMO), multi-input single-output (MISO) and serial-propagated structure. The most commonly used multi-layer feed-forward networks with conjugate gradient algorithm are adopted for application. Rainfall—runoff data sets from two watersheds in Taiwan are used separately to investigate the effectiveness and stability of the neural networks for MSA flood forecasting. The results indicate consistently that, even though the MIMO is the most common architecture presented in ANNs, it is less accurate because its multi-objectives (predicted many time steps) must be optimized simultaneously. Both MISO and serial-propagated neural networks are capable of performing accurate short-term (one- or two-step-ahead) forecasting. For long-term (more than two steps) forecasts, only the serial-propagated neural network could provide satisfactory results in both watersheds. The results suggest that the serial-propagated structure can help in improving the accuracy of MSA flood forecasts.
    Relation: Hydrological Sciences Journal 52(1), pp.114-130
    DOI: 10.1623/hysj.52.1.114
    Appears in Collections:[Graduate Institute & Department of Water Resources and Environmental Engineering] Journal Article

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