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2025 02 v.41 1-9
基于社交文本的洪涝信息抽取与时空演变分析
基金项目(Foundation): 国家重点研发计划项目(2022YFB3904202); 国家自然科学基金重大项目(42394063)
邮箱(Email): z.xu@qq.com;
DOI:
中文作者单位:

西南交通大学地球科学与工程学院;

摘要(Abstract):

以2018年寿光市水灾为例,基于深度学习和规则匹配相结合方法从微博数据中抽取关键灾情信息,通过时间序列分析提取洪涝事件的关键时间节点,利用核密度估计探索灾情的空间分布特征,应用HDBSCAN算法分析核心受灾区域;通过LDA算法对待响应点的文本进行主题分析,提取不同受灾区域面临的问题与需求;基于抽取的水情相关信息和DEM数据,利用HAND模型绘制洪水淹没范围,识别核心受灾区。实验结果表明,该框架的灾情信息抽取总体准确率达83%,高于其他对比方法,可为应急响应提供定向援助与资源配置的决策依据。

关键词(KeyWords): 社交文本;深度学习;规则匹配;LDA模型;HAND模型
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基本信息:

DOI:

中图分类号:P426.616;P208

引用信息:

[1]侯华伟,慎利,贾嘉楠等.基于社交文本的洪涝信息抽取与时空演变分析[J].地理与地理信息科学,2025,41(02):1-9.

基金信息:

国家重点研发计划项目(2022YFB3904202); 国家自然科学基金重大项目(42394063)

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