Machine Learning Insights into the Last 400 Years of Etna Lateral Eruptions from Historical Volcanological Data

Abstract

Volcanic hazard assessment is generally based on past eruptive behavior, assuming that previous activity is representative of future activity. Hazard assessment can be supported by Artificial Intelligence (AI) techniques, such as machine learning, which are used for data exploration to identify features of interest in the data. Here, we applied a machine learning technique to automate the analysis of these datasets, handling intricate patterns that are not easily captured by explicit commands. Using the k-means clustering algorithm, we classified effusive eruptions of Mount Etna over the past 400 years based on key parameters, including lava volume, Mean Output Rate (MOR), and eruption duration. Our analysis identified six distinct eruption clusters, each characterized by unique eruption dynamics. Furthermore, spatial analysis revealed significant sectoral variations in eruption activity across Etna’s flanks. These findings, derived through unsupervised learning, offer new insights into Etna’s eruptive behavior and contribute to the development of hazard maps that are essential for long-term spatial planning and risk mitigation.