地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (12): 2329-2339.doi: 10.12082/dqxxkx.2023.230236

• SpatialDI 2023 会议优秀论文 • 上一篇    下一篇

融合多源空间数据的城市暴雨级联灾害情景态势转化推演方法

刘昭阁1,*(), 李向阳2, 朱晓寒3   

  1. 1.厦门大学 公共事务学院,厦门 361005
    2.哈尔滨工业大学 经济与管理学院,哈尔滨 150001
    3.武汉东湖新技术开发区管委会,武汉 430075
  • 收稿日期:2023-04-28 修回日期:2023-06-16 出版日期:2023-12-25 发布日期:2023-12-05
  • 作者简介:刘昭阁(1992— ),男,山东烟台人,博士,硕士生导师,研究方向为应急管理大数据分析与社会治理智能化方法。 E-mail: zhaogeliu@xmu.edu.cn
  • 基金资助:
    国家自然科学基金大数据驱动的管理与决策研究重大研究计划项目(91746207);国家自然科学基金面上项目(71774043);教育部人文社会科学研究青年基金项目(22YJC630095)

A Method for Urban Rainstorm Cascading Disaster Scenario Converting Deduction by Integrating Multi-Source Spatial Data

LIU Zhaoge1,*(), LI Xiangyang2, ZHU Xiaohan3   

  1. 1. School of Public Affairs, Xiamen University, Xiamen 361005, China
    2. School of Management, Harbin Institute of Technology, Harbin 150001, China
    3. Administrative Committee of Wuhan East Lake High-tech Development Zone, Wuhan 430075, China
  • Received:2023-04-28 Revised:2023-06-16 Online:2023-12-25 Published:2023-12-05
  • Contact: *LIU Zhaoge, E-mail: zhaogeliu@xmu.edu.cn
  • Supported by:
    Major Research Plan of the National Natural Science Foundation of China named Big data Driven Management and Decision-making Research(91746207);General Program of the National Natural Science Foundation of China(71774043);Humanities and Social Sciences Foundation of the Ministry of Education of China(22YJC630095)

摘要:

城市级联灾害情景态势的转化演化是指在情景态势演化过程中,承灾体在致灾因子作用下转化为新的致灾因子,形成灾害链。针对城市暴雨级联灾害情景态势的转化演化问题,本文基于级联灾害情景态势相关的多源空间数据,结合概率化分析工具,提出一种暴雨级联灾害应对的情景态势转化推演方法。① 基于历史案例确定级联情景涉及情景要素及潜在要素转化路径;② 在百度百科和维基百科的网络知识资源支持下,利用机器学习中的分组最小角回归方法选择情景要素特征;③考虑级联灾害情景态势演化过程中的多阶段及复杂关联特征,构建情景态势转化推演的动态贝叶斯网络模型;④ 利用马尔可夫链蒙特卡罗方法对贝叶斯网络进行求解。将上述情景态势转化推演方法应用于武汉市高新区的暴雨应对实践,用例结果表明:本文方法能够结合历史案例和网络数据,实现关键情景要素及其特征的快速有效生成,帮助提升情景态势转化推演的可靠性;同时,支持地理网格等小粒度承灾体的情景态势转化推演,有助于更加精准的暴雨应急决策支持,在可视化分析方面亦具有较好效果。

关键词: 城市暴雨, 级联灾害, 情景推演, 态势转化, 空间数据, 机器学习, 历史案例, 动态贝叶斯网络

Abstract:

The converting evolution of cascading disaster scenario refers to that in the process of disaster scenario evolution, the disaster bearing bodies transform into new disaster hazards, forming a disaster chain. Rainstorm can easily cause serious secondary disasters such as waterlogging, debris flow and flood, and the combination of these secondary disasters will make the city more vulnerable. However, existing research on rainstorm cascading scenario deduction lacks the analysis of specific scenario evolution situations such as multi disaster combination, scenario element converting, and human-induced emergencies. Meanwhile, traditional research often relies on the probability inference based on existing scenario evolution networks, without providing a construction method for scenario evolution networks, making it difficult to adapt to the knowledge requirements of actual scenario situation converting deduction. To address the scenario converting evolution problems of urban rainstorm cascading disasters, this paper proposes a scenario converting deduction method for rainstorm cascading disaster response based on multi-source spatial data and probability analysis tools. First, based on local and non-local historical emergency cases, the scenario elements involved in the rainstorm cascading disaster scenarios and their potential converting paths are identified. Next, with the support of Baidu Encyclopedia and Wikipedia network knowledge resources, relevant scenario element features and their associations are extracted, and a Group Lasso machine learning method is adopted to achieve feature selection of involved scenario elements. Then, considering the multi-stage and complex scenario correlation in the process of cascading scenario evolution, a dynamic Bayesian network model for scenario converting deduction is constructed. Finally, a Markov chain Monte Carlo method is used to solve the Bayesian network and generate the converting probabilities. The proposed method is applied to the rainstorm response practice of Wuhan High-tech Zone. The use case results show that the proposed method can combine historical cases and network data to achieve rapid and effective generation of key scenario elements and their features, helping to improve the reliability of scenario converting deduction. At the same time, the proposed method supports the scenario converting deduction of small-scale disaster-bearing bodies such as geographic grids, which helps to provide more accurate rainstorm emergency decision-making support and provide good performance in visual analysis. The uncertainty analysis of the proposed method shows that the precision of original probabilities of key scenario element features and the size of generated geographic grids significantly affect the scenario converting deduction results. These findings provide important information for the local area and are expected to help the rainstorm disaster management of other jurisdictions.

Key words: urban rainstorms, cascading disaster, scenario deduction, scenario converting, spatial data, machine learning, historical cases, dynamic Bayesian network