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

TL;DR

This study applies a machine learning clustering algorithm to a large dataset to identify and validate extreme rainfall events in Sicily, aiming to improve climate change response strategies.

Contribution

First application of the Affinity Propagation algorithm to detect extreme rainfall events in Sicily using a comprehensive high-frequency dataset.

Findings

Confirmed recent anomalous rainfall events in eastern Sicily

Validated detection results with weather indicators

Demonstrated potential for policy-making improvements

Abstract

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.