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A multi-modal machine learning approach to detect extreme rainfall events in Sicily
Eleonora Vitanza1, Giovanna Maria Dimitri1, Chiara Mocenni2
1Department of Information Engineering and Mathematics, University of Siena, Via Roma, 56, 53100, Siena, Italy.
Scientific Reports
|April 16, 2023
Summary
Extreme rainfall events in Sicily were identified using machine learning. The Rainfall Sicily Extreme dataset and Affinity Propagation algorithm helped detect these anomalous events, crucial for climate change adaptation strategies.
Area of Science:
- Environmental Science
- Data Science
- Climatology
Background:
- Extreme rainfall events, like the 2021 deluge near Catania, Sicily, have severe environmental, social, and economic impacts.
- Such intense precipitation events are increasing globally, necessitating effective detection methods for mitigation.
- Understanding local extreme rainfall is vital for planning adaptive strategies against intensified future climate scenarios.
Purpose of the Study:
- To apply machine learning for the first time to detect extreme rainfall areas in Sicily.
- To introduce and validate the use of the Affinity Propagation algorithm for analyzing high-frequency rainfall data.
- To establish a foundation for data-driven policy-making in climate change adaptation.
Main Methods:
- Utilized the Affinity Propagation clustering algorithm, a machine learning technique.
- Applied the algorithm to the high-frequency Rainfall Sicily Extreme (RSE) dataset (2009-2021).
- Validated the detected extreme rainfall areas using established weather indicators.
Main Results:
- Successfully identified areas experiencing extreme rainfall in Sicily using the Affinity Propagation algorithm.
- The analysis confirmed recent anomalous rainfall events, particularly in eastern Sicily.
- The RSE dataset provided a robust foundation for detecting these localized, intense weather phenomena.
Conclusions:
- The study demonstrates the efficacy of machine learning, specifically Affinity Propagation, in detecting extreme rainfall events.
- Data science techniques offer practical tools for policy-making to address climate change impacts.
- This approach can significantly improve regional planning and disaster preparedness for extreme weather.
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