View source for A Machine Learning approach to evaluate coastal risks related to extreme weather events in the Veneto region (Italy)

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'''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 Living Technology
Authors: Maria Katherina Dal Barco, Margherita Maraschini, Davide M. Ferrario, Ngoc Diep Nguyen, Silvia Torresan, Sebastiano Vascon, Andrea Critto

'''Description''': 

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 during the 2009–2019 timeframe, 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.

'''Keywords''':

MLP; Risk assessment; Coast; Impact

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