A single framework for assessing flash flood and landslide susceptibility: an application to the Mediterranean Liguria region, Italy
4 affiliations
- 1Department of Catchment and Urban Hydrology, Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands
- 2Department of Safe and Resilient Infrastructure, Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands
- 3Department of Civil and Environmental Engineering, Carleton University, K1S 5B6 Ottawa, Canada
- 4Department of Soil, Water and Structures, Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands
Abstract
Flash floods and landslides have caused severe economic damages and loss of life, especially in mountainous regions. To support effective risk management there is a growing interest in muti-hazard assessment. In this study a globally applicable Machine Learning (ML) Framework for landslide susceptibility mapping was extended to allow for the assessment of both landslide and flash flood susceptibility. The Framework was applied and evaluated in the Italian region Liguria that is frequently and severely impacted by both hazards. A relatively dense inventory of past events was constructed to facilitate the training of the ML Framework. The analysis revealed substantial similarities in the causative factors for the two hazards. There is a considerable area of Liguria susceptible to both hazards, although flash floods most often occur in river valleys whereas landslide susceptibility is also high in the upper courses of river catchments. We found a very high susceptibility along the coastline where many villages and cities are located. The unified framework allows for the integration of different hazard types under a consistent modelling structure. This enhances the comparability of results and supports the development of integrated mitigation strategies for any region of interest.
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BibTeX
@article{riveros2026single,
title = {{A single framework for assessing flash flood and landslide susceptibility: an application to the Mediterranean Liguria region, Italy}},
author = {Alessia Riveros and Chamidu Gunaratne and Mario Martinelli and Frederiek Sperna Weiland},
journal = {Natural hazards and earth system sciences},
year = {2026},
month = jun,
doi = {10.5194/nhess-26-2437-2026},
url = {https://doi.org/10.5194/nhess-26-2437-2026},
note = {CC BY},
}
RIS
TY - JOUR
TI - A single framework for assessing flash flood and landslide susceptibility: an application to the Mediterranean Liguria region, Italy
AU - Alessia Riveros
AU - Chamidu Gunaratne
AU - Mario Martinelli
AU - Frederiek Sperna Weiland
PY - 2026
DA - 2026/06/01
JO - Natural hazards and earth system sciences
DO - 10.5194/nhess-26-2437-2026
UR - https://doi.org/10.5194/nhess-26-2437-2026
AB - Flash floods and landslides have caused severe economic damages and loss of life, especially in mountainous regions. To support effective risk management there is a growing interest in muti-hazard assessment. In this study a globally applicable Machine Learning (ML) Framework for landslide susceptibility mapping was extended to allow for the assessment of both landslide and flash flood susceptibility. The Framework was applied and evaluated in the Italian region Liguria that is frequently and severely impacted by both hazards. A relatively dense inventory of past events was constructed to facilitate the training of the ML Framework. The analysis revealed substantial similarities in the causative factors for the two hazards. There is a considerable area of Liguria susceptible to both hazards, although flash floods most often occur in river valleys whereas landslide susceptibility is also high in the upper courses of river catchments. We found a very high susceptibility along the coastline where many villages and cities are located. The unified framework allows for the integration of different hazard types under a consistent modelling structure. This enhances the comparability of results and supports the development of integrated mitigation strategies for any region of interest.
SN - 1561-8633
SN - 1684-9981
ER -