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Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data

Author

Listed:
  • Gerasimos Antzoulatos

    (Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece)

  • Ioannis-Omiros Kouloglou

    (Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece)

  • Marios Bakratsas

    (Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece)

  • Anastasia Moumtzidou

    (Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece)

  • Ilias Gialampoukidis

    (Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece)

  • Anastasios Karakostas

    (Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece)

  • Francesca Lombardo

    (Eastern Alps River Basin District Authority (AAWA), Cannaregio 4314, 30121 Venice, Italy)

  • Roberto Fiorin

    (Eastern Alps River Basin District Authority (AAWA), Cannaregio 4314, 30121 Venice, Italy)

  • Daniele Norbiato

    (Eastern Alps River Basin District Authority (AAWA), Cannaregio 4314, 30121 Venice, Italy)

  • Michele Ferri

    (Eastern Alps River Basin District Authority (AAWA), Cannaregio 4314, 30121 Venice, Italy)

  • Andreas Symeonidis

    (School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Stefanos Vrochidis

    (Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece)

  • Ioannis Kompatsiaris

    (Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH), 57001 Thessaloniki, Greece)

Abstract

Flooding is one of the most destructive natural phenomena that happen worldwide, leading to the damage of property and infrastructure or even the loss of lives. The escalation in the intensity and number of flooding events as a result of the combination of climate change and anthropogenic factors motivates the need to adopt real-time solutions for mapping flood hazards and risks. In this study, a methodological framework is proposed that enables the assessment of flood hazard and risk levels of severity dynamically by fusing optical remote sensing (Sentinel-1) and GIS-based data from the region of the Trieste, Monfalcone and Muggia Municipalities. Explainable machine learning techniques were utilised, aiming to interpret the results for the assessment of flood hazard. The flood inventory was randomly divided into 70 % , used for training, and 30 % , employed for testing. Various combinations of the models were evaluated for the assessment of flood hazard. The results revealed that the Random Forest model achieved the highest F1-score (approx. 0.99), among others utilised for generating flood hazard maps. Furthermore, the estimation of the flood risk was achieved by a combination of a rule-based approach to estimate the exposure and vulnerability with the dynamic assessment of flood hazard.

Suggested Citation

  • Gerasimos Antzoulatos & Ioannis-Omiros Kouloglou & Marios Bakratsas & Anastasia Moumtzidou & Ilias Gialampoukidis & Anastasios Karakostas & Francesca Lombardo & Roberto Fiorin & Daniele Norbiato & Mic, 2022. "Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data," Sustainability, MDPI, vol. 14(6), pages 1-28, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3251-:d:768221
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    Citations

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    Cited by:

    1. Marcela Bindzarova Gergelova & Ludovit Kovanič & Hany F. Abd-Elhamid & Anton Cornak & Miroslav Garaj & Radovan Hilbert, 2023. "Evaluation of Spatial Landscape Changes for the Period from 1998 to 2021 Caused by Extreme Flood Events in the Hornád Basin in Eastern Slovakia," Land, MDPI, vol. 12(2), pages 1-24, February.
    2. Zihui Dai & Alireza Arabameri & Peyman Yariyan & Hasan Raja Naqvi & Tania Nasrin, 2025. "Optimizing Flood Susceptibility Detection Using Ensemble Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(13), pages 7109-7132, October.
    3. Noor Suraya Romali & Sumiliana Sulong & Akiyuki Kawasaki, 2025. "A Systematic Review of Flood Damage Assessment: Insight for the Data-Scarce Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(10), pages 4707-4734, August.
    4. Chuanrong Zhang & Xinba Li, 2025. "AI-Enhanced Remote Sensing of Land Transformations for Climate-Related Financial Risk Assessment in Housing Markets: A Review," Land, MDPI, vol. 14(8), pages 1-39, August.
    5. Pandian Mangan & Dinagarapandi Pandi & Mohd Anul Haq & Aniruddha Sinha & Rajagopal Nagarajan & Twinkle Dasani & Ismail Keshta & Mohammed Alshehri, 2022. "Analytic Hierarchy Process Based Land Suitability for Organic Farming in the Arid Region," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    6. Treicy Renata Belló Bengua & Guilherme Garcia de Oliveira, 2026. "Hazard mapping of hydrological disasters in the municipality of Porto Alegre/RS," 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. 122(8), pages 1-26, April.
    7. Maelaynayn El baida & Mohamed Hosni & Farid Boushaba & Mimoun Chourak, 2024. "A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(15), pages 5823-5864, December.

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