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首页> 外文期刊>Journal of the American Water Resources Association >ANN OUTPUT UPDATING OF LUMPED CONCEPTUAL RAINFALL/RUNOFF FORECASTING MODELS
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ANN OUTPUT UPDATING OF LUMPED CONCEPTUAL RAINFALL/RUNOFF FORECASTING MODELS

机译：集总概念降雨/径流预报模型的ANN输出更新

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摘要

Artificial neural networks (ANNs) are tested for the output updating of one-day-ahead and three-day-ahead streamflow forecasts derived from three lumped conceptual rainfall/runoff (R-R) models: the GR4J, the IHAC, and the TOPMO. ANN output updating proved superior to a parameter updating scheme and to the 'simple' output updating scheme, which always replicates the last observed forecast error. In fact, ANN output updating was able to compensate for large differences in the initial performance of the three tested lumped conceptual R-R models, which the other tested updating approaches were not able to achieve. This is done mainly by incorporating input vectors usually exploited for ANN R-R modeling such as previous rainfall and streamflow observations, in addition to the previous observed error. For one-day-ahead forecasts, the performance of all three lumped conceptual R-R models, used in conjunction with ANN output updating, was equivalent to that of the ANN R-R model. For three-day-ahead forecasts, the performance of the ANN-output-updated conceptual models was even superior to that of the ANN R-R model, revealing that the conceptual models are probably performing some tasks that the ANN R-R model cannot map. However, further testing is needed to substantiate the last statement.
机译：测试了人工神经网络（ANN），以从三种集总概念性降雨/径流（R-R）模型（GR4J，IHAC和TOPMO）得出的提前一天和提前三天流量预测的输出更新。事实证明，人工神经网络输出更新优于参数更新方案和“简单”输出更新方案，后者总是复制最后观察到的预测误差。实际上，ANN输出更新能够补偿三种经过测试的集总概念R-R模型的初始性能的巨大差异，而其他经过测试的更新方法则无法实现这些差异。这主要是通过合并通常用于ANN R-R建模的输入向量来完成的，例如先前的观测误差和先前的降雨和水流观测。对于未来一天的预测，与ANN输出更新结合使用的所有三个集总概念R-R模型的性能均与ANN R-R模型的性能相同。对于提前三天的预测，ANN输出更新的概念模型的性能甚至优于ANN R-R模型的性能，这表明概念模型可能正在执行ANN R-R模型无法映射的某些任务。但是，需要进一步测试以证实最后的陈述。

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