Journal
SUSTAINABILITY
Volume 14, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/su14063251
Keywords
flood hazard; flood risk maps; flood susceptibility; satellite imagery analysis; crisis maps; machine learning
Funding
- European Union [832876, 101004157]
- H2020 Societal Challenges Programme [832876] Funding Source: H2020 Societal Challenges Programme
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Flooding is a destructive natural phenomenon worldwide, and real-time solutions are needed to assess flood hazards and risks. By combining remote sensing and GIS data, flood hazards can be effectively evaluated, and flood hazard maps can be generated using machine learning techniques.
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.
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