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In 2021 300 mm of rain, nearly half the average annual rainfall, fell nearCatania (Sicily island, Italy). Such events took place in just a few hours,with dramatic consequences on the environmental, social, economic, and healthsystems of the region. This is the reason why, detecting extreme rainfallevents is a crucial prerequisite for planning actions able to reverse possiblyintensified dramatic future scenarios. In this paper, the Affinity Propagationalgorithm, a clustering algorithm grounded on machine learning, was applied, tothe best of our knowledge, for the first time, to identify excess rain eventsin Sicily. This was possible by using a high-frequency, large dataset wecollected, ranging from 2009 to 2021 which we named RSE (the Rainfall SicilyExtreme dataset). Weather indicators were then been employed to validate theresults, thus confirming the presence of recent anomalous rainfall events ineastern Sicily. We believe that easy-to-use and multi-modal data sciencetechniques, such as the one proposed in this study, could give rise tosignificant improvements in policy-making for successfully contrasting climatechanges.

A Multi-Modal Machine Learning Approach to Detect Extreme Rainfall Events in Sicily