Predicción de precipitaciones extremas mediante aprendizaje supervisado en regiones específicas

Abstract

Abstract: Currently, global climate change, a result of pollution, has intensified extreme weather events, especially extreme rainfall, which can trigger floods, landslides, and other environmental emergencies. This problem represents a growing challenge for authorities. Early prediction of extreme rainfall is essential to reduce the negative effects of these events on the population and the environment. This research proposes the use of the KDD methodology to predict extreme rainfall using Supervised Learning algorithms. A review of related work will be conducted to define variables, better models, and metrics used for this specific problem, experimenting with historical climate datasets from the main meteorological APIs. The results are compared using.