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

Gerasimos Antzoulatos, Ioannis-Omiros Kouloglou, Marios Bakratsas, Anastasia Moumtzidou, Ilias Gialampoukidis, Anastasios Karakostas, Francesca Lombardo, Roberto Fiorin, Daniele Norbiato, Michele Ferri, Andreas Symeonidis, Stefanos Vrochidis and Ioannis Kompatsiaris
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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

Sustainability, 2022, vol. 14, issue 6, 1-28

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.

Keywords: flood hazard; flood risk maps; flood susceptibility; satellite imagery analysis; crisis maps; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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