Abstract
Climate change and biodiversity loss are two of the most pressing challenges of recent decades. While the former has received significant attention from both the scientific community and the public, the latter often remains in the background. Nevertheless, the two phenomena are deeply interconnected. Through advanced data analytics, machine learning, and predictive modeling, Artificial Intelligence (AI) offers powerful tools to address these global issues. In this work, we present an application of a statistical method, namely the clustering of compound events, to demonstrate how the joint analysis of two or more interacting factors can yield more meaningful insights from large datasets, such as those derived from climatological observations.
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PDFDOI: http://dx.doi.org/10.2423/i22394303v15Sp47
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