A Multi-Modal Machine Learning Approach to Detect Extreme Rainfall Events in Sicily
This addresses the need for detecting extreme rainfall to aid policy-making against climate change impacts in Sicily, but it is incremental as it applies an existing algorithm to a new dataset.
The paper tackled the problem of detecting extreme rainfall events in Sicily, such as a 2021 event with 300 mm of rain in hours, by applying the Affinity Propagation algorithm for the first time to identify these events using a high-frequency dataset from 2009 to 2021, and validated results with weather indicators confirming recent anomalies in eastern Sicily.
In 2021 300 mm of rain, nearly half the average annual rainfall, fell near Catania (Sicily island, Italy). Such events took place in just a few hours, with dramatic consequences on the environmental, social, economic, and health systems of the region. This is the reason why, detecting extreme rainfall events is a crucial prerequisite for planning actions able to reverse possibly intensified dramatic future scenarios. In this paper, the Affinity Propagation algorithm, a clustering algorithm grounded on machine learning, was applied, to the best of our knowledge, for the first time, to identify excess rain events in Sicily. This was possible by using a high-frequency, large dataset we collected, ranging from 2009 to 2021 which we named RSE (the Rainfall Sicily Extreme dataset). Weather indicators were then been employed to validate the results, thus confirming the presence of recent anomalous rainfall events in eastern Sicily. We believe that easy-to-use and multi-modal data science techniques, such as the one proposed in this study, could give rise to significant improvements in policy-making for successfully contrasting climate changes.