西南交通大学地球科学与工程学院;
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。
302 | 0 | 1822 |
下载次数 | 被引频次 | 阅读次数 |
[1] 顾西辉,张强,张生.1961~2010 年中国农业洪旱灾害时空特征、成因及影响[J].地理科学,2016,36(3):439-447.
[2] NOJI E K.The public health consequences of disasters[J].Prehospital and Disaster Medicine,2000,15(4):21-31.
[3] TALBOT C J,BENNETT E M,CASSELL K,et al.The impact of flooding on aquatic ecosystem services[J].Biogeochemistry,2018,141:439-461.
[4] 林珲,吴贤宇,潘家祎,等.中国城市洪涝实时预报研究:现状与挑战[J].测绘学报 2022,51(7):1306.
[5] 邬柯杰,吴吉东,叶梦琪.社交媒体数据在自然灾害应急管理中的应用研究综述[J].地理科学进展,2020,39(8):1412-1422.
[6] 吴建华,胡烈云,赵宇,等.基于BiLSTM-CRF与分类分层标注的微博中突发事件时空信息精细识别方法[J].地理与地理信息科学,2021,37(3):1-8.
[7] 叶鹏,张春菊,刘欣,等.基于事件过程建模的台风灾害社交媒体信息聚合与演变特征表达[J].地理与地理信息科学,2024,40(2):11-18.
[8] YAN L,PEDRAZA-MARTINEZ A J.Social media for disaster management:operational value of the social conversation[J].Production and Operations Management,2019,28(10):2514-2532.
[9] ZHANG Y,CHEN Z Q,ZHENG X,et al.Extracting the location of flooding events in urban systems and analyzing the semantic risk using social sensing data[J].Journal of Hydrology,2021,603:127053.
[10] 伍智超,王超,李秉清,等.基于社交媒体数据的武汉内涝时空统计分析[J].测绘地理信息,2022,47(5):89-92.
[11] NKWUNONWO U C,WHITWORTH M,BAILY B.A review of the current status of flood modelling for urban flood risk management in the developing countries[J].Scientific African,2020,7:e00269.
[12] LIN A Q,WU H,LIANG G H,et al.A big data-driven dynamic estimation model of relief supplies demand in urban flood disaster[J].International Journal of Disaster Risk Reduction,2020,49:101682.
[13] 富璇,闫浩文,王小龙,等.城市内涝场景下的微地图制作方法[J].地球信息科学学报,2024,26(5):1166-1179.
[14] 周锐.基于社交媒体的城市内涝灾害信息实时挖掘与分析[D].武汉:华中科技大学,2021.
[15] VONGKUSOLKIT J,HUANG Q.Situational awareness extraction:a comprehensive review of social media data classification during natural hazards[J].Annals of GIS,2021,27(1):5-28.
[16] BAI X S,LIU X X,LU S H,et al.SEPM:rapid seism emergency information processing based on social media[J].Natural Hazards,2020,104:659-679.
[17] ARTHUR R,BOULTON C A,SHOTTON H,et al.Social sensing of floods in the UK[J].PloS one,2018,13(1):e0189327.
[18] ZHANG T,SHEN S,CHENG C X,et al.A topic model based framework for identifying the distribution of demand for relief supplies using social media data[J].International Journal of Geographical Information Science,2021,35(11):2216-2237.
[19] 沈伟豪,钟燕飞,王俊珏,等.多模态数据的洪涝灾害知识图谱构建与应用[J].武汉大学学报(信息科学版),2023,48(12):2009-2018.
[20] FANG J,HU J M,SHI X W,et al.Assessing disaster impacts and response using social media data in China:a case study of 2016 Wuhan rainstorm[J].International journal of disaster risk reduction,2019,34:275-282.
[21] ARAPOSTATHIS S G.A methodology for automatic acquisition of flood-event management information from social media:the flood in Messinia,South Greece,2016[J].Information Systems Frontiers,2021,23(5):1127-1144.
[22] KARMEGAM D,MAPPILLAIRAJU B.Spatio-temporal distribution of negative emotions on Twitter during floods in Chennai,India,in 2015:a post hoc analysis[J].International Journal of Health Geographics,2020,19:1-13.
[23] 杨腾飞,解吉波,闫东川,等.基于深度学习的社交媒体情感信息抽取及其在灾情分析中的应用研究[J].地理与地理信息科学,2020,36(2):62-68.
[24] TYSHCHUK Y,WALLACE W A.Modeling human behavior on social media in response to significant events[J].IEEE Transactions on Computational Social Systems,2018,5(2):444-457.
[25] BROUWER T,EILANDER D,VAN LOENEN A,et al.Probabilistic flood extent estimates from social media flood observations[J].Natural Hazards and Earth System Sciences,2017,17(5):735-747.
[26] YAN Z Y,GUO X G,ZHAO Z L,et al.Achieving fine-grained urban flood perception and spatio-temporal evolution analysis based on social media[J].Sustainable Cities and Society,2024,101:105077.
[27] 吕雪锋,陈思宇.自然灾害网络舆情信息分析与管理技术综述[J].地理与地理信息科学,2016,32(4):49-56.
[28] 胡段牧,袁武,牛方曲,等.中文文本蕴含气象灾害事件信息多模型融合抽取方法[J].地球信息科学学报,2022,24(12):2342-2355.
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)