对城市公共安全环境基础设施和社会风险的综合评估

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Sicheng Liu¹

¹Department of International Relations, Faculty of International Management and Business, Budapest Business University, Budapest, 1165, Hungary. 15517001105@163.com.

Scientific reports

|January 21, 2026

概括

这项研究整合了GIS,MCDA和机器学习来评估城市风险,揭示了高风险地区和有效的缓解策略,如基础设施升级. 该框架有助于决策者加强城市安全和恢复力,特别是对弱势社区.

科学领域:

城市规划和环境科学
空间分析和风险评估.
计算社会科学计算社会科学

背景情况:

城市公共安全受到相互关联的环境,基础设施和社会风险的影响.
快速的城市化加剧了这些重叠的风险在大都市地区.
要了解和管理复合城市风险,需要采用全面的方法.

研究的目的:

开发一个综合框架,使用GIS,MCDA和机器学习来评估复合城市风险.
确定高风险的城市地区,并了解跨领域的风险相互作用.
评估缓解策略的有效性,并为公平的政策决策提供信息.

主要方法:

综合框架结合了地理信息系统 (GIS),多标准决策分析 (MCDA) 和机器学习模型.
使用的空间数据集包括土地表面温度,空气质量,排水,建筑条件,人口统计和犯罪.
使用渐变增强决策树 (GBDT) 和时间卷积网络 (TCN),具有k-fold和时间块验证.

主要成果:

在复合城市风险评估方面取得了强的预测性能 (AUC=0.91,RMSE=6.2).
确定了河流邻的定居点和沿着中央运输走廊的高风险集群.
模拟表明,结构升级和绿色基础设施可以将复合风险降低22-30%,使低收入社区受益.

结论:

开发的框架提供了一个可扩展的,数据驱动的方法来诊断和减轻复合城市风险.
调查结果为决策者提供了可操作的见解,以优先考虑加强城市安全和恢复力的干预措施.
权益加权分析表明,有可能改善弱势群体的结果.

关键词:

综合风险评估是一种综合风险评估.GIS MCDADA 已经开始了.机器学习机器学习适应性规划适应性规划城市公共安全城市公共安全

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