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Application of Self-Organizing Feature Maps to Water Resources Projects

机译：自组织特征图在水资源工程中的应用

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The self-organizing feature maps (SOFM) is a kind of ANNs (Artificial Neural Networks) method which is capable of clustering, classification, estimation, prediction, and data mining in a wide-spread range of disciplines. Two types of complex water resources related applications, namely, watershed hydrology and coastal storm surge, are demonstrated here. The former application is to find the best match watershed from a large knowledge base of over on thousand quantifying watersheds and to determine the reliability of "transplant" watershed information during the clustering processes while the later application uses SOFM adequately characterize the storm surge response, and provide a means for reliably estimating surge response for storms not simulated with a selected physics-based surge model. The computational procedures and results are presented.
机译：自组织特征图（SOFM）是一种ANN（人工神经网络）方法，能够在广泛学科中进行聚类，分类，估计，预测和数据挖掘。这里展示了两种与水资源复杂相关的应用程序，即流域水文学和沿海风暴潮。前一个应用程序是从超过数千个量化分水岭的大型知识库中找到最匹配的分水岭，并确定聚类过程中“移植”分水岭信息的可靠性，而后一个应用程序则使用SOFM充分表征了风暴潮响应。提供了一种方法，可以可靠地估计未使用选定的基于物理的浪涌模型模拟的风暴的浪涌响应。介绍了计算过程和结果。

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