Hou Huawei, Shen Li, Jia Jianan, Xu Zhu
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China.
Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China.
Sci Total Environ. 2024 Nov 1;949:174948. doi: 10.1016/j.scitotenv.2024.174948. Epub 2024 Jul 25.
Flood disasters cause significant casualties and economic losses annually worldwide. During disasters, accurate and timely information is crucial for disaster management. However, remote sensing cannot balance temporal and spatial resolution, and the coverage of specialized equipment is limited, making continuous monitoring challenging. Real-time disaster-related information shared by social media users offers new possibilities for monitoring. We propose a framework for extracting and analyzing flood information from social media, validated through the 2018 Shouguang flood in China. This framework innovatively combines deep learning techniques and regular expression matching techniques to automatically extract key flood-related information from Weibo textual data, such as problems, floodings, needs, rescues, and measures, achieving an accuracy of 83 %, surpassing traditional models like the Biterm Topic Model (BTM). In the spatiotemporal analysis of the disaster, our research identifies critical time points during the disaster through quantitative analysis of the information and explores the spatial distribution of calls for help using Kernel Density Estimation (KDE), followed by identifying the core affected areas using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. For semantic analysis, we adopt the Latent Dirichlet Allocation (LDA) algorithm to perform topic modeling on Weibo texts from different regions, identifying the types of disasters affecting each township. Additionally, through correlation analysis, we investigate the relationship between disaster rescue requests and response measures to evaluate the adequacy of flood response measures in each township. The research results demonstrate that this analytical framework can accurately extract disaster information, precisely identify critical time points in flood disasters, locate core affected areas, uncover primary regional issues, and further validate the sufficiency of response measures, therefore enhancing the efficiency in collecting disaster information and analytical capabilities.
每年，洪水灾害在全球范围内都会造成重大人员伤亡和经济损失。在灾害发生期间，准确及时的信息对于灾害管理至关重要。然而，遥感技术无法兼顾时间和空间分辨率，且专业设备的覆盖范围有限，这使得持续监测具有挑战性。社交媒体用户分享的实时灾害相关信息为监测提供了新的可能性。我们提出了一个从社交媒体中提取和分析洪水信息的框架，并通过2018年中国寿光洪水进行了验证。该框架创新性地将深度学习技术与正则表达式匹配技术相结合，从微博文本数据中自动提取与洪水相关的关键信息，如问题、洪水情况、需求、救援和措施等，准确率达到83%，超过了双词主题模型（BTM）等传统模型。在灾害的时空分析中，我们的研究通过对信息的定量分析确定灾害期间的关键时间点，并使用核密度估计（KDE）探索求救信息的空间分布，随后使用基于密度的带有噪声的空间聚类层次算法（HDBSCAN）识别核心受灾区域。对于语义分析，我们采用潜在狄利克雷分配（LDA）算法对不同地区的微博文本进行主题建模，确定影响每个乡镇的灾害类型。此外，通过相关性分析，我们研究灾害救援请求与应对措施之间的关系，以评估每个乡镇洪水应对措施的充分性。研究结果表明，这个分析框架能够准确提取灾害信息，精确识别洪水灾害中的关键时间点，定位核心受灾区域，揭示主要区域问题，并进一步验证应对措施的充分性，从而提高灾害信息收集效率和分析能力。
