Appraisal of Urban Waterlogging and Extent Damage Situation after the Devastating Flood

The rapid urbanization in Pakistan frequently leads to urban waterlogging due to storms. The event often leads to significant harm to the environment, people, and urban economies. Early identification of rainstorm events and urban waterlogging disasters is essential in reducing associated damages. Twitter (X), a widely used global microblogging platform, offers a large amount of real-time tweets that can be used for immediate monitoring purposes. This study introduces a method for recognizing microblogs with information about urban rainstorms and waterlogging and uses blog posts to assess the waterlogging risk. In light of the preliminary examination of microblog content, we determine the efficacy of cluster and support vector machine methods for classification. In addition to text vector attributes, we incorporate sentiment aspects to improve the precision and clarity of our results. We also constructed a lexicon for waterlogging severity to evaluate the risk of waterlogging based on the content of Tweets. Afterward, we generate a risk map using ArcGIS, with findings suggesting that SVM is suitable for detecting rainstorms and waterlogging events in real time. The waterlogging location aligns with the findings of the hazard assessment. The proposed risk assessment method can be a precise tool for promptly addressing emergencies.