A Machine Learning approach to evaluate coastal risks related to extreme weather events in the Veneto region (Italy): Difference between revisions
Created page with "<div style="text-align:justify"> '''Year of publication''': 2024 '''Access''': Open access publication '''Link''': https://doi.org/10.1016/j.ijdrr.2024.104526 '''Organisation(s) / Author(s)''': Organisations: Centro Euro-Mediterraneo sui Cambiamenti Climatici, Risk Assessment and Adaptation Strategies Division; Ca' Foscari University of Venice, Department of Environmental Sciences, Informatics and Statistics; Ca’ Foscari University of Venice, European Center for Liv..." |
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|Publication Year=2024 | |||
|Access=Open | |||
|Link=https://doi.org/10.1016/j.ijdrr.2024.104526 | |||
|Author(s)=Dal Barco, M.K., Maraschini, M., Ferrario, D.M., Nguyen, N.D., Torresan, S., Vascon, S., Critto, A. | |||
|Organisation(s)=Department of Environmental Sciences, Ca’ Foscari University of Venice; Fondazione Centro Euro-Mediterraneo Sui Cambiamenti Climatici (CMCC) | |||
|Description=To assess impacts caused by extreme events (storm surges, extreme precipitation, wind events) in the Veneto coastal municipalities, a machine learning (ML) approach was developed to understand connections between atmospheric and marine hazards and impacts recorded by the Veneto region emergency archive between 2009–2019, identifying the most influencing factors triggering multiple risks. Additionally, the coastal municipalities were clustered considering the intrinsic relationships between impact occurrences, exposure and vulnerability features. Several algorithms were compared to estimate daily risk of impacts to occur providing hazards, exposure and vulnerability information. The proposed algorithm was designed as support tool to increase the understanding of impacts’ occurrence in coastal areas, thus helping the adaptation planning process. The abstract is edited from the original journal article (Dal Barco et al., 2024: https://www.sciencedirect.com/science/article/pii/S2212420924002887), which is published in Open Access by Elsevier Ltd. | |||
|Technical Considerations=It is recommended to retrain or fine tune the algorithm regularly with new data, to consider changes in the dynamics, or new mitigation structures in place. | |||
|Key Words=MLP; Risk assessment; Coast; Impact | |||
}} | |||
To assess impacts caused by extreme events (storm surges, extreme precipitation, wind events) in the Veneto coastal municipalities, a ML approach was developed to understand connections between atmospheric and marine hazards and impacts recorded by the Veneto region emergency archive | |||
The abstract is edited from the original journal article (Dal Barco et al., 2024: https://www.sciencedirect.com/science/article/pii/S2212420924002887), which is published in Open Access by Elsevier Ltd. | |||
It is recommended to retrain or fine tune the algorithm regularly with new data, to consider changes in the dynamics, or new mitigation structures in place. | |||
MLP; Risk assessment; Coast; Impact | |||
Latest revision as of 15:09, 4 April 2025
Author(s): Dal Barco, M.K., Maraschini, M., Ferrario, D.M., Nguyen, N.D., Torresan, S., Vascon, S., Critto, A.
Organisation(s)/Authors: Department of Environmental Sciences, Ca’ Foscari University of Venice; Fondazione Centro Euro-Mediterraneo Sui Cambiamenti Climatici (CMCC)
Description:
To assess impacts caused by extreme events (storm surges, extreme precipitation, wind events) in the Veneto coastal municipalities, a machine learning (ML) approach was developed to understand connections between atmospheric and marine hazards and impacts recorded by the Veneto region emergency archive between 2009–2019, identifying the most influencing factors triggering multiple risks. Additionally, the coastal municipalities were clustered considering the intrinsic relationships between impact occurrences, exposure and vulnerability features. Several algorithms were compared to estimate daily risk of impacts to occur providing hazards, exposure and vulnerability information. The proposed algorithm was designed as support tool to increase the understanding of impacts’ occurrence in coastal areas, thus helping the adaptation planning process. The abstract is edited from the original journal article (Dal Barco et al., 2024: https://www.sciencedirect.com/science/article/pii/S2212420924002887), which is published in Open Access by Elsevier Ltd.
Technical Considerations:
It is recommended to retrain or fine tune the algorithm regularly with new data, to consider changes in the dynamics, or new mitigation structures in place.
Key Words:
MLP; Risk assessment; Coast; Impact