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良好的人员避险方案是降低城市内涝灾害消极影响的有效途径之一,是提高城市市政管理水平的科学手段。针对城市极端天气雨水径流路线随机性强的特点,同时考虑到传统人员避险模型在极端天气随机性突发情况下的迟钝性现象,提出了一种基于特征筛选数据挖掘路径决策模型的城市内涝人员避险机制。选取城市内涝区域在流动过程中的路径特点加入到模型中,运用聚类方法对极端天气城市内涝中人员避险路径特征进行优选,构建优化数据挖掘决策支持分析模型,进而得出城市内涝灾害中人员避险路径。试验结果表明,该算法能够提高极端天气城市内涝灾害中人员避险路径选择的准确度。
A good staff hedging program is one of the effective ways to reduce the negative impact of waterlogging disaster in cities and is a scientific means to improve the level of city administration. In view of the randomness of runoff course of urban extreme weather and rainstorm, taking into account the sluggishness of the traditional safe-haven model in case of extreme weather stochastic suddenness, this paper proposes a city waterlogging based on feature selection data mining decision-making model Personnel hedging mechanism. This paper chooses the characteristic of the path of the urban waterlogging area in the process of flowing into the model, selects the characteristic of hedging path in the waterlogging of extreme weather city by using the clustering method, constructs the optimization data mining decision support analysis model, and then draws the conclusion that urban waterlogging disaster Personnel hedging path. Experimental results show that this algorithm can improve the accuracy of hedging path selection in waterlogging disaster in extreme weather cities.