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

Shan-e-hyder Soomro (), Muhammad Waseem Boota (), Xiaotao Shi (), Gul-e-Zehra Soomro (), Yinghai Li (), Muhammad Tayyab (), Caihong Hu (), Chengshuai Liu (), Yuanyang Wang (), Junaid Abdul Wahid (), Mairaj Hyder Alias Aamir Soomro (), Jiali Guo () and Yanqin Bai ()
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Shan-e-hyder Soomro: China Three Gorges University
Muhammad Waseem Boota: Henan University
Xiaotao Shi: China Three Gorges University
Gul-e-Zehra Soomro: Quaid-e-Awam University of Engineering
Yinghai Li: China Three Gorges University
Muhammad Tayyab: China Three Gorges University
Caihong Hu: Zhengzhou University
Chengshuai Liu: Zhengzhou University
Yuanyang Wang: China Three Gorges University
Junaid Abdul Wahid: Zhengzhou University
Mairaj Hyder Alias Aamir Soomro: University of Wollongong
Jiali Guo: China Three Gorges University
Yanqin Bai: China Three Gorges University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 12, No 22, 4931 pages

Abstract: Abstract 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.

Keywords: Disaster Communication; Risk Perception; Social Media (X); Sensitivity; Karachi Cosmopolitan city; Pakistan (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03894-w

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