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Machine Learning for Flood Susceptibility Mapping of the Chennai Floods of 2023

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Rapid and precise urban flood forecasting is crucial for promptly implementing preventive actions. This article presents a review of the literature focusing on soft computing techniques and algorithms for predicting urban floods. We also present two famous machine learning algorithms viz., Random Forest (RF) and XGBoost to create a Flood Susceptibility Map (FSM) for the city of Chennai in the state of Tamil Nadu, India based on the historical data of the 2023 floods of Chennai. A decent classification accuracy of 65% and 70% has been achieved using our approach for both RF and XGBoost algorithms respectively.

    Original languageEnglish
    Title of host publicationProceedings - 2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems
    Subtitle of host publicationHarmonizing Signals, Data, and Energy: Bridging the Digital Future, SPICES 2024
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9798350376135
    DOIs
    Publication statusPublished - 2024
    Event2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024 - Kottayam, India
    Duration: 20-09-202422-09-2024

    Publication series

    NameProceedings - 2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems: Harmonizing Signals, Data, and Energy: Bridging the Digital Future, SPICES 2024

    Conference

    Conference2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024
    Country/TerritoryIndia
    CityKottayam
    Period20-09-2422-09-24

    All Science Journal Classification (ASJC) codes

    • Artificial Intelligence
    • Computer Networks and Communications
    • Computer Science Applications
    • Information Systems
    • Signal Processing
    • Information Systems and Management
    • Energy Engineering and Power Technology

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