Recently, the ceaseless rise in the global average temperature has led to extreme climates in which natural disasters, such as droughts, hurricanes, earthquakes and floods, are becoming increasingly serious. Recent research has found that social media typically reflects disasters earlier than official communication channels. In this study, the idea of collecting information on flood disasters caused during the periods of typhoons and heavy rains for a city from the plain text messages released by social media by means of a term frequency (TF) and sliding window approach is proposed. The dataset analysed here contains a total of 292 articles and 12,484 tweets. This research determines how to establish a warning mechanism, with an added notification time for flooding disasters, and it shows how to provide relevant disaster relief personnel with references. This article contributes by combining social media data with emergency management information cloud (EMIC) data, especially in the context of having a mechanism for warning about flooding disasters. According to the experimental results, a sliding window of 90 min and a sliding gap of 10 min obtained the best F-measure value (F = 0.315). The event studied was Typhoon Megi (September 2016), which caused major flooding in Tainan. For the Typhoon Megi event, the flood disaster location database had 161 streets available for matching. Based on the experimental results, it is possible to obtain a high-precision (90% or higher) accuracy rate from real-time tweet data by exploiting a social media dataset.

最近，全球平均温度的不断升高导致了极端气候，干旱，飓风，地震和洪水等自然灾害变得越来越严重。最近的研究发现，社交媒体通常比官方传播渠道更早地反映灾难。在这项研究中，提出了通过术语频率（TF）和滑动窗口方法从社交媒体发布的纯文本消息中收集有关城市台风和大雨期间造成的洪水灾害信息的想法。此处分析的数据集总共包含292条文章和12,484条推文。这项研究确定了如何建立预警机制，并为洪水灾害增加了通知时间，并且说明了如何为相关的救灾人员提供参考。本文通过将社交媒体数据与紧急情况管理信息云（EMIC）数据相结合而做出了贡献，特别是在具有针对洪水灾害的警告机制的情况下。根据实验结果，最佳滑动时间为90分钟，滑动间隙为10分钟F测量值（F  = 0.315）。所研究的事件是台风梅吉（2016年9月），它在台南造成了严重洪灾。对于台风梅吉事件，洪水灾难地点数据库具有161条可匹配的街道。根据实验结果，可以通过利用社交媒体数据集从实时推文数据获得高精度（90％或更高）的准确率。