基于最大熵模型的城市内涝风险预测: 以北京市主城区为例
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国家自然科学基金项目(面上项目,重点项目,重大项目)


Urban Flood Risk Prediction Based on Maximum Entropy Model - A Case Study of Beijing"s Main Urban Area
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    摘要:

    随着全球气候变化和城市化发展,城市内涝频发且严重影响城市发展,研究内涝影响因素,进行内涝风险评估对防灾减灾具有重要意义。以北京市主城区为研究区域,获取内涝空间数据和影响因子数据,采用MaxEnt模型进行探究,识别内涝潜在风险区和分析影响因子与内涝风险的关系,结果表明:(1)影响内涝风险的主导因子为离立交桥的距离、建筑密度、不透水面比例、人口密度、暴雨持续时间、NDVI;(2)研究区内超过24.6%区域范围都处于内涝风险区,高风险区总面积约为40.17 km2,中风险区的总面积约为298.09 km2较低风险与低风险区面积分别为423.75 km230.53 km2;(3)各区内涝点分布数量大小关系为丰台区>海淀区>朝阳区>石景山区>西城区、东城区;历史内涝点在空间分布上具有“南密北疏,西密东疏”的分布格局与“多核中心,次中心团带连接”的特征;(4)对风险评估结果进行空间自相关分析发现风险概率在丰台区中部,海淀区东北部、南部,石景山区东部地区呈高—高聚集,表明该区域在未来可能会受到周围区域的影响而发生内涝灾害,要高度关注该区域实现精准防控。相关成果为城市进行基础设施完善改造、潜在内涝积水点防治、制定应急减灾预案和应对措施等方面提供一定参考。

    Abstract:

    With global climate change and urbanization development, urban flooding occurs frequently and seriously affects urban development, so it is important to study the impact factors of waterlogging and carry out waterlogging risk assessment for disaster prevention and mitigation. Taking the main urban area of Beijing as the study area, we obtain the spatial data of waterlogging and the data of influencing factors, and use the MaxEnt model to explore, identify the potential risk area of waterlogging and analyze the relationship between influencing factors and the risk of waterlogging, and the results show that: (1) The dominant factors affecting the risk of flooding are distance from overpasses, building density, percentage of impervious surface, population density, storm duration, and NDVI; (2) More than 24.6% of the study area is at risk of flooding, with a total area of about 40.17 km2 in the high-risk zone and 298.09 km2 in the medium-risk zone; the lower-risk and low-risk zones cover 423.75 km2 and 30.53 km2, respectively; (3) the distribution of the number of flooding points in each district size relationship for Fengtai District > Haidian District > Chaoyang District > Shijingshan District > Xicheng District, Dongcheng District; historical flooding points in the spatial distribution of the distribution pattern of the "dense south and sparse north, dense west and sparse east," and "multi-core centers, sub-centers connected to the band" characteristics; (4) Spatial autocorrelation analysis of the risk assessment results found that the probability of risk in the middle of Fengtai District, the northeastern and southern part of Haidian District, and the eastern part of Shijingshan District is a high-high aggregation, indicating that the region may be affected by the surrounding areas and flooding disasters occur in the future, and that we should pay close attention to the region to achieve precise prevention and control. The results provide a certain reference for cities to carry out infrastructure improvement and renovation, prevention and control of potential flooding and waterlogging points, and the development of emergency mitigation plans and countermeasures.

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张震禹,刘家福,祝悦,等. 基于最大熵模型的城市内涝风险预测: 以北京市主城区为例[J]. 科学技术与工程, 2024, 24(13): 5652-5661.
Zhang Zhenyu, Liu Jiafu, Zhu Yue, et al. Urban Flood Risk Prediction Based on Maximum Entropy Model - A Case Study of Beijing"s Main Urban Area[J]. Science Technology and Engineering,2024,24(13):5652-5661.

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  • 收稿日期:2023-06-21
  • 最后修改日期:2024-02-28
  • 录用日期:2023-10-10
  • 在线发布日期: 2024-05-17
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