Clustering of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts
Clustering of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts
复制标题
用于评估和可能改进水流预测集总神经网络模型的异质降水场聚类
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
C. W. Baxter
中科院分区：
文献类型：
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作者：
N. Lauzon;F. Anctil;C. W. Baxter
抽象的。这项工作解决的问题，更好地考虑集总径流模型中，只有面积平均降水量通常被用作输入的降水场的异质性。提出了一种利用Kohonen神经网络对降水场进行聚类的方法。在此基础上，对集总径流模型的一日预报性能进行了评价和改进。采用多层感知器神经网络作为集总径流模型。在法国的巴斯-恩-巴塞特流域，这是配备了23个雨量计的数据，为21年的时间，作为应用案例。结果表明，建议的聚类方法，产生的降水场组，是在协议的全球气候特征影响该地区，以及地形的限制流域（即，orography）。通过分析各模型在降水场聚类方面的性能，指出了各模型的优缺点。结果还表明，多层感知器神经网络的能力，以考虑降水的异质性，即使是作为集总总径流模型。
Abstract. This work addresses the issue of better considering the heterogeneity of precipitation fields within lumped rainfall-runoff models where only areal mean precipitation is usually used as an input. A method using a Kohonen neural network is proposed for the clustering of precipitation fields. The evaluation and improvement of the performance of a lumped rainfall-runoff model for one-day ahead predictions is then established based on this clustering. Multilayer perceptron neural networks are employed as lumped rainfall-runoff models. The Bas-en-Basset watershed in France, which is equipped with 23 rain gauges with data for a 21-year period, is employed as the application case. The results demonstrate the relevance of the proposed clustering method, which produces groups of precipitation fields that are in agreement with the global climatological features affecting the region, as well as with the topographic constraints of the watershed (i.e., orography). The strengths and weaknesses of the rainfall-runoff models are highlighted by the analysis of their performance vis-a-vis the clustering of precipitation fields. The results also show the capability of multilayer perceptron neural networks to account for the heterogeneity of precipitation, even when built as lumped rainfall-runoff models.