期刊
ENVIRONMENTAL MODELLING & SOFTWARE
卷 19, 期 4, 页码 357-368出版社
ELSEVIER SCI LTD
DOI: 10.1016/S1364-8152(03)00135-X
关键词
rainfall-runoff; conceptual model; artificial neural network; stream flow prediction; model performance
Although attractive to hydrologists, artificial neural network modeling still lacks norms that would help modelers to create and train efficient rainfall-runoff models in a systematic way. This study focuses on the impact of the length of observed records on the performance of multiple-layer perceptrons (MLPs), and compare their results with those of a parsimonious conceptual model equipped with an updating scheme. Both models were assessed for 1-day-ahead stream flow predictions. Ninety-two different model scenarios were obtained for 1-, 3-, 5-, 9-, and 15-year time sub-series created from a 24-year training set, shifting by a 1-year sliding window. All the model scenarios were verified against the same 7-year test set. The results revealed that MLP stream flow mapping was efficient as long as wet weather data were available for the training; the longer series implicitly guarantee that the data contain valuable information of the hydrological behavior; the results were consistent with those reported for conceptual rainfall-runoff models. The physical knowledge in the conceptual models allowed them to make much better use of 1-year training sets than the MLPs. However, longer training sets were more beneficial to the MLPs than to the conceptual model. Both types shared best performance about evenly for 3- and 5-year training sets, but MLPs did better whenever the training set was dominated by wet weather. The MLPs continued to improve for input vectors of 9 years and more, which was not the case of the conceptual model. (C) 2003 Elsevier Ltd. All rights reserved.
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