Artificial Intelligence for Climate Change Multi-Risk Assessment
Year of publication: In progress
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Organisation(s) / Author(s): Davide Ferreira, Ngoc Diep Nguyen et al.
Description
With extreme climate events becoming more frequent and severe, it’s crucial to have effective methods for assessing multiple risks at once. Traditional approaches often struggle to untangle the complex interactions between different hazards and how they affect vulnerability and exposure. To develop better strategies for reducing risks and preparing for disasters, we need to understand how extreme climate events impact both society and nature.
That’s where Artificial Intelligence (AI) comes in. AI is a powerful tool for analyzing large amounts of environmental data, bringing together information from various sources, and understanding complex relationships.
In a recent study, researchers have created a step-by-step framework using AI to assess the risks posed by extreme climate events in the Veneto Region of North-East Italy. Here’s how it works:
- They use statistical methods and machine learning to identify unusual patterns in climate data, pinpointing extreme events like heatwaves, droughts, storm surges, heavy rainfall, and strong winds.
- They combine these patterns to create sets of multi-hazard events, considering factors like timing and location overlap. This helps them understand how different hazards might compound or occur in sequence.
- They use supervised machine learning algorithms to model how susceptible different areas are to various combinations of hazards. By training the model with data from past events, they can identify which vulnerability and exposure factors are most important and pinpoint areas at high risk of multiple hazards.
This comprehensive approach, tested as part of the Myriad-EU project in the Veneto Region, provides valuable insights into the complex dynamics of multi-risk events. It helps us assess and predict the impacts of these events under different climate change scenarios, ultimately aiding in better decision-making and disaster planning.
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