刘野,逯跃锋,孟庆祥,秦永,刘聪,闻俏,胡玉龙

• 1:山东理工大学
• 2:中国科学院地理科学与资源研究所
• 3:武汉大学
• 4:中国交通通信信息中心

摘要(Abstract)：

为了综合分析城市积水点的时空动态及其风险等级,该文将水文模型与深度神经网络(DNN)相结合,提出了耦合水文模型和神经网络的城市暴雨积水点的风险评价模型。应用水文模型计算降雨过程中积水点的动态积水量以及积水区域;使用缓冲区分析来提取积水点附近道路的兴趣点(POI)。然后利用积水量、积水范围和POI的信息,采用深层神经网络对积水点风险进行评价;利用贵阳市中心城区的内涝调查数据对模拟结果进行实验验证。结果表明,该模型能够比较精确地评估城市积水灾害风险等级及其时空分布。

关键词(KeyWords)： 积水点;风险评估;水文模型;深度神经网络

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(91646207);; 国家重点研发计划项目(2017YFC0822000,2017YFC1405000)

作者(Author): 刘野,逯跃锋,孟庆祥,秦永,刘聪,闻俏,胡玉龙

DOI: 10.16251/j.cnki.1009-2307.2020.08.026

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