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Mapping and analysing framework for extreme precipitation-induced flooding

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Abstract

A conceptual framework is proposed, to identify flood affected locations that should be considered in order to lessen the consequences of naturally occurring disaster. Sentinel-1 data are used to evaluate the performance of automatic Otsu’s method and machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine (SVM), CART, Minimum Distance (MD), K-nearest neighbour (KNN) and KD Tree KNN (KD-KNN)) to characterise flooded region. The study provided a holistic spatial assessment of flood inundation in the region due to impact of the extreme precipitation. The most adequate performance based on compound value is achieved by KNN (\({C}_{v}=2\)) followed by SVM (\({C}_{v}=2.25\)) ML model and Otsu’s thresholding method (\({C}_{v}=2.5\)). The validation site results reveal that Vertical transmit and Vertical received (VV) polarization performs significantly better than Vertical transmit and Horizontal received (VH) polarization. The most accurate flood extent produced by Otsu’s thresholding method (overall accuracy of 94.98%) and MD (overall accuracy of 88.98%) are used to evaluate the indicative number of individuals and buildings at risk within the study areas using Gridded Population of the World Version 4 (GPWv4), Global ML Building Footprints by Microsoft and OpenStreetMap building data.

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Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Acknowledgements

The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work, under the Research Groups Funding program grant code (NU/RG/SERC/12/21).

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  1. Nguyet-Minh Nguyen

    Present address: Laboratory of Environmental Sciences and Climate Change, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam

Authors and Affiliations

  1. Water Resources Engineering and Management Institute, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Baroda, Gujarat, India

    Vikas Kumar Rana

  2. Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200, Sosnowiec, Poland

    Nguyen Thi Thuy Linh

  3. Center of Excellence in Sustainable Disaster Management, School of Engineering and Technology, Walailak University, Nakhon Si Thammarat, 80161, Thailand

    Pakorn Ditthakit

  4. Civil Engineering Department, College of Engineering, Najran University, Najran, 66291, Saudi Arabia

    Ismail Elkhrachy

  5. HUTECH Institute of Applied Sciences, HUTECH University, 475A, Dien Bien Phu, Ward 25, Binh Thanh District, Ho Chi Minh City, Vietnam

    Trinh Trong Nguyen

  6. Laboratory of Environmental Sciences and Climate Change, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam

    Trinh Trong Nguyen

  7. Faculty of Environment, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam

    Nguyet-Minh Nguyen

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  1. Vikas Kumar Rana
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Contributions

Vikas Kumar Rana, Nguyen Thi Thuy Linh: Formal Analysis, Investigation, Methodology, and Writing—original draft. Pakorn Ditthakit, Ismail Elkhrachy: Resources, writing—review & editing, Trinh Trong Nguyen, Nguyen Nguyet Minh: Formal analysis, investigation, supervision, validation, and writing—review & editing. All authors have read and agreed to the published version of the manuscript.

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Rana, V.K., Linh, N.T.T., Ditthakit, P. et al. Mapping and analysing framework for extreme precipitation-induced flooding. Earth Sci Inform 16, 4213–4234 (2023). https://doi.org/10.1007/s12145-023-01137-x

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