| 基于自组织映射神经网络的水库洪水过程分类 |
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| 引用本文： | 林康聆,周研来,陈华,郭生练,王俊.基于自组织映射神经网络的水库洪水过程分类[J].水科学进展,2022,33(6):934-943. |
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| 作者姓名： | 林康聆 周研来 陈华 郭生练 王俊 |
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| 作者单位： | 武汉大学水资源与水电工程科学国家重点实验室, 湖北 武汉 430072 |
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| 基金项目： | 国家重点研发计划资助项目2021YFC3200303国家自然科学基金资助项目U20A20317 |
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| 摘 要： | 针对传统洪水分类方法中洪水特征提取时存在信息损失和主观性强的问题, 本文基于洪水全过程构建自组织映射神经网络(Self-Organizing Map, SOM), 综合考虑代表性和拓扑性等评价指标以优选网络规模, 实现洪水全过程的拓扑逻辑关系挖掘及分类。以三峡水库洪水过程为研究对象, 研究结果表明: ① 2×3维SOM覆盖率达到56.7%, 与3×3维SOM相比, 仅有约2%的覆盖率差距, 具有良好代表性; 2×3维SOM输出层仅有1处翻转, 拓扑结构比3×3维SOM更优, 更适合三峡水库洪水过程分类。② 2×3维SOM将洪水过程划分为6类, 其神经元拓扑结构可有效刻画各分类的差异与联系, 说明SOM可基于可视化拓扑逻辑关系实现高维洪水数据的可靠客观分类。③与传统方法的历史典型洪水分类结果相比, SOM能提供可靠且丰富的分类信息。
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| 关 键 词： | 洪水分类 自组织映射神经网络 数据挖掘 长江流域 三峡水库 |
| 收稿时间： | 2022-06-16 |
Classification of reservoir flood processes based on self-organizing map neural networks |
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| Affiliation: | State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China |
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| Abstract: | In this study, self-organizing map (SOM) neural networks were constructed based on entire flood processes for reducing information loss and subjectivity compared to traditional flood classification methods. Considering evaluation indicators of representativeness and topology, we identified the optimal dimensionality of the SOM for characterizing the topological logic relationship of flood processes and realizing a new flood classification. The inflow flood process of the Three Gorges Reservoir (TGR), located along the main stream of the Yangtze River, served as a study case. Our results showed the following. ① The coverage ratio indicator and the gap of a 2×3-dimensional SOM were 56.7% and 2%, respectively, of those of a 3×3-dimensional SOM; besides, the former SOM had only one flip and its topology was better than that of the latter. Due to the good performance of the 2×3-dimensional SOM, this was identified as the most suitable for the classification of TGR flood processes. ② Based on the 2×3-dimensional SOM, the flood processes registered at the Yichang station were divided into 6 classes. The differences between classes were reflected by neuron topology, indicating that this SOM can provide reliable flood classification results from high-dimensional flood data by visualizing the neuron topology. ③ Compared with traditional classification methods, which have been applied to investigate historical typical floods, the proposed 2×3-dimensional SOM was found to be more reliable and to provide more useful information. |
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