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

Author

Listed:
  • Shan-e-hyder Soomro

    (China Three Gorges University
    Zhengzhou 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)

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.

Suggested Citation

  • 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 Aa, 2024. "Appraisal of Urban Waterlogging and Extent Damage Situation after the Devastating Flood," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4911-4931, September.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:12:d:10.1007_s11269-024-03894-w
    DOI: 10.1007/s11269-024-03894-w
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    References listed on IDEAS

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    1. Steven Koop & Cornelis Leeuwen, 2015. "Assessment of the Sustainability of Water Resources Management: A Critical Review of the City Blueprint Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5649-5670, December.
    2. Mahdi Valikhan Anaraki & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2021. "Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 199-223, January.
    3. Heidi Kreibich & Anne F. Loon & Kai Schröter & Philip J. Ward & Maurizio Mazzoleni & Nivedita Sairam & Guta Wakbulcho Abeshu & Svetlana Agafonova & Amir AghaKouchak & Hafzullah Aksoy & Camila Alvarez-, 2022. "The challenge of unprecedented floods and droughts in risk management," Nature, Nature, vol. 608(7921), pages 80-86, August.
    4. Bikram Manandhar & Shenghui Cui & Lihong Wang & Sabita Shrestha, 2023. "Urban Flood Hazard Assessment and Management Practices in South Asia: A Review," Land, MDPI, vol. 12(3), pages 1-29, March.
    5. Xiaoliang Xie & Bingqi Xie & Jiaqi Cheng & Qi Chu & Thomas Dooling, 2021. "A simple Monte Carlo method for estimating the chance of a cyclone impact," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2573-2582, July.
    6. Yang Xiao & Beiqun Li & Zaiwu Gong, 2018. "Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(2), pages 833-842, November.
    7. Bairong Wang & Jun Zhuang, 2017. "Crisis information distribution on Twitter: a content analysis of tweets during Hurricane Sandy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(1), pages 161-181, October.
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