Beyond Accuracy: A Comparative Study of Machine Learning Models for Extreme Weather Forecasting in Rio de Janeiro with a Green AI Perspective - Breder, Gabriel B.; Vasconcelos, Gyslla; Ferro, Mariza (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional, 2025)
In recent years, climate change has severely impacted the city of Rio de Janeiro. Some extreme events have occurred, such as temperatures above 40° C and heavy rains that have caused landslides, floods, and deaths. This study investigates the application of Machine Learning (ML) models (MLP, XGBoost, and LSTM) to predict extreme temperature and precipitation for the city of Rio de Janeiro, while critically evaluating their environmental impact through a Green AI perspective. Beyond traditional accuracy metrics like RMSE, our aim is to assess the trade-off between good prediction and computational efficiency, energy consumption, CO2 emissions, and water usage during model training.