Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space
Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space
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DOI：
10.1029/2006wr005142
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发表时间：
2007-07
影响因子：
5.4
通讯作者：
C. Shu;T. Ouarda
中科院分区：
文献类型：
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作者：
C. Shu;T. Ouarda
典型相关分析（CCA）和人工神经网络（ANN）的基础上开发的模型，以获得改进的洪水分位数估计在无资料的网站。CCA是用来形成一个典型的地文空间使用的网站特征从测量网站。然后，人工神经网络模型应用于识别洪水分位数和CCA空间中的地文变量之间的函数关系。建立了两种神经网络模型：单神经网络模型和集成神经网络模型。所提出的方法适用于151集水区在加拿大魁北克省。两个评估程序，刀切验证程序和分裂样本验证程序，被用来评估所提出的模型的性能。所提出的模型的结果进行了比较与原始CCA模型，典型克里金模型，和原始的人工神经网络模型。结果表明，基于CCA的ANN模型提供了优于原始ANN模型的估计。神经网络集成方法提供了更好的泛化能力比单一的神经网络模型。在预测精度方面，基于CCA的集成ANN模型在所有模型中具有最佳性能。
Models based on canonical correlation analysis (CCA) and artificial neural networks (ANNs) are developed to obtain improved flood quantile estimates at ungauged sites. CCA is used to form a canonical physiographic space using the site characteristics from gauged sites. Then ANN models are applied to identify the functional relationships between flood quantiles and the physiographic variables in the CCA space. Two ANN models, the single ANN model and the ensemble ANN model, are developed. The proposed approaches are applied to 151 catchments in the province of Quebec, Canada. Two evaluation procedures, the jackknife validation procedure and the split sample validation procedure, are used to evaluate the performance of the proposed models. Results of the proposed models are compared with the original CCA model, the canonical kriging model, and the original ANN models. The results indicate that the CCA‐based ANN models provide superior estimation than the original ANN models. The ANN ensemble approaches provide better generalization ability than the single ANN models. The CCA‐based ensemble ANN model has the best performance among all models in terms of prediction accuracy.