| 靳文波,刘永强,房玉东,王棚飞,薛明,刘磊磊.链生灾害多源数据分析与灾害趋势研究——以“杜苏芮”台风为例[J].水利学报,2025,56(8):1012-1024 |
| 链生灾害多源数据分析与灾害趋势研究——以“杜苏芮”台风为例 |
| Research on multi-source data analysis and disaster trend of chain-induced disasters： A case study of typhoon "DuSuri" |
| 投稿时间：2024-01-30 修订日期：2025-08-18 |
| DOI：10.13243/j.cnki.slxb.20240064 |
| 中文关键词: 台风灾害链 指挥调度 多源数据 数据分析 数据应用 |
| 英文关键词: typhoon disaster chain command and dispatch multi-source data data analysis data application |
| 基金项目:国家重点研发计划项目（2022YFC3090603）；北京市教育委员会科技计划项目（KZ202211417049） |
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| 中文摘要: |
| 2023年夏季，受台风“杜苏芮”登陆以及台风水汽北上与冷空气交绥影响，我国东南、华东、华北、东北地区遭受了严重的自然灾害。本文从典型台风灾害链出发，以数据关联实证视角分析了灾害事故调度过程中使用的高等级气象预警、灾情上报、铁塔监测、交通监测、舆情热度等多源异构数据时空区域分布特性与时空变化特性，并比对各类信息与实际灾害严重程度变化的趋势相关性与时间偏离性。结果表明：铁塔监测数据峰值与灾情峰值较为贴近，相比于其他类型数据偏移量要少0.35~2.21 d，可用于快速分析自然灾害整体受灾形势；交通监测数据曲线与灾损曲线贴合度最高，在山洪、地质灾害、流域性洪水等灾害过程中可较好反映出当地灾损变化与灾后恢复情况。以交通大数据监测、铁塔大数据监测等信息作核心要素可为灾区灾情研判、灾后重建评估等提供辅助依据。 |
| 英文摘要: |
| In the summer of 2023，due to the landing of typhoon 'Dusuri' and the interaction between typhoon water vapor northward and cold air，China’s southeast，East，North and northeast regions suffered serious natural disasters. Based on the typical typhoon disaster chain，this paper analyzes the spatio-temporal distribution and variation characteristics of multi-source heterogeneous data such as high-level meteorological early warning，disaster reporting，tower monitoring，traffic monitoring and public opinion heat used in the process of disaster accident scheduling from the perspective of data correlation，and compares the trend correlation and time deviation between various types of information and the change of actual disaster severity. The results show that the peak value of the tower monitoring data is close to the peak value of the disaster，and the offset is 0.35-2.21d less than that of other types of data，which can be used to quickly analyze the overall disaster situation of natural disasters. The traffic monitoring data curve has the highest degree of it with the disaster loss curve and can better reflect the local disaster loss changes and postdisaster recovery in the process of mountain torrents，geological disasters，watershed floods，and other disasters. Using traffic big data monitoring，tower big data monitoring，etc. as core elements can provide auxiliary data for disaster assessment and post-disaster reconstruction. |
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