A spatial assessment of urban waterlogging risk based on a Weighted Naïve Bayes classifier.
A spatial assessment of urban waterlogging risk based on a Weighted Naïve Bayes classifier.
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DOI：
10.1016/j.scitotenv.2018.02.172
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Xianzhe Tang;Yuqin Shu;Y. Lian;Yaolong Zhao;Yingchun Fu
中科院分区：
文献类型：
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作者：
Xianzhe Tang;Yuqin Shu;Y. Lian;Yaolong Zhao;Yingchun Fu
城市内涝发生频繁，往往造成相当大的破坏，严重影响自然环境、人类生活和社会经济。城市内涝风险空间评价是预防城市内涝、减少内涝损失的重要分析步骤。加权Naïve贝叶斯（WNB）分类器是一种在不确定条件下进行知识发现和概率推理的强大方法；WNB分类器可用于估计危险的可能性。在分析过程中，考虑在WNB中加入6个空间因子，可提高城市内涝风险的预测效率。基于此，本文以中国广州主城区为例，开发了一个将WNB与GIS相结合的空间框架来评估城市内涝风险。结果表明：1)根据条件概率表和权重确定了6个空间因子的合理性；2)最准确抽样表具有客观性；③内涝风险高可能性区主要位于研究区西南部。东北地区相对没有内涝风险。研究结果揭示了更准确的城市内涝风险空间格局，可用于识别风险“热点”。网格化估算结果为城市内涝相关决策提供了现实参考。
Urban waterlogging occurs frequently and often causes considerable damage that seriously affects the natural environment, human life, and the social economy. The spatial evaluation of urban waterlogging risk represents an essential analytic step that can be used to prevent urban waterlogging and minimize related losses. The Weighted Naïve Bayes (WNB) classifier is a powerful method for knowledge discovery and probability inference under conditions of uncertainty; a WNB classifier can be applied to estimate the likelihood of hazards. Six spatial factors were considered to be added to the WNB, which may improve the efficiency in predicting urban waterlogging risk during analysis. As such, a spatial framework integrating WNB with GIS was developed to assess the risk of urban waterlogging using the primary urban area of Guangzhou in China as an example. The results show that 1) the rationality of six spatial factors was determined according to the Conditional Probability Tables and weights; 2) the Most Accurate Sampling Table has objectivity; and 3) the areas with a high likelihood of waterlogging risk were mainly located in the southwestern part of the study area. The northeastern zones are relatively free of waterlogging risk. The results reveal a more accurate spatial pattern of urban waterlogging risk that can be used to identify risk “hot spots”. The resulting gridded estimates provide a realistic reference for decision making related to urban waterlogging.