An innovative tool, combining Artificial Intelligence (AI) and Machine Learning (ML), is now available to study and mitigate the impacts of extreme weather events on coastlines. Developed from a study involving the CMCC (Euro-Mediterranean Center on Climate Change), this prototype offers decision-makers new capabilities to evaluate hazard factors and devise effective adaptation strategies.
Coastal areas, characterized by high population density, interconnected economic activities, and fragile ecosystems, are particularly susceptible to extreme weather events, which are increasing due to climate change. The complexity of these interactions necessitates improved methodologies for assessing risks.
Among the first studies of its kind, titled “A machine learning approach to evaluate coastal risks related to extreme weather events in the Veneto region (Italy)”, the research utilizes AI to estimate risks from extreme weather events in the coastal municipalities of the Veneto region. This study showcases how ML models can be effectively employed for environmental and multi-risk assessment under climate change conditions.
ML Coastal Risks
The graphical abstract of the study by Dal Barco et al. (2024) demonstrates the model’s capabilities. “The model developed in the study represents an early prototype decision support tool underpinning climate change risk assessment and the definition of adaptation strategies at the regional scale,” says Maria Katherina Dal Barco, CMCC researcher and lead author of the study.
Key Findings and Implications
The ML model identifies the primary drivers of risk in the Veneto region, including total daily precipitation, wind intensity, and maximum sea surface height. It also highlights how the importance of these hazards varies by municipality due to the diverse geographical patterns of the coast.
“This application was aimed to give support to decision-makers in the development of early warning systems and adaptation plans,” explains Dal Barco. The study emphasizes the necessity to identify the factors that have historically generated these risks, either individually or in combination, and to determine, based on the risk score value, whether a sample is at high risk of impact. Additionally, the study incorporates exposure and vulnerability factors, reflecting the heterogeneous nature of the Veneto coastal areas and how they could amplify the effects of extreme weather events.
Future Directions and Benefits of ML in Risk Assessment
ML algorithms offer a new approach to tackling multi-risk events by effectively handling vast amounts of heterogeneous data and modeling complex non-linear relationships between multiple factors and feedback mechanisms. This capability is crucial for developing early warning systems and robust adaptation plans, enhancing resilience against the increasing threat of extreme weather events.
This innovative AI tool represents a significant advancement in coastal risk assessment, providing decision-makers with the insights needed to protect vulnerable coastal areas and their communities.