Estimating Extreme Rainfall Equation Parameter in Southeast Brazil Using Machine Learning

Abstract

Purpose: This article explores the use of advanced machine learning techniques, including Random Forests and Deep Learning, to predict parameters of the intense rainfall equation.   Methods: The study applies deep neural networks and random forests to predict parameters of the intense rainfall equation. Random Forests method is employed to handle the heterogeneity of data, while Deep Learning captures non-linear relationships. The application takes place in the state of Rio de Janeiro, with a focus on predicting parameters for specific municipalities using available data from the ANA (Brazilian Water Agency).   Results and Conclusion:  The neural network demonstrates accuracy in learning these parameters, with discrepancies attributed to differences in historical data periods. Despite limitations, the neural network shows promise in predictions, while the Random Forest closely aligns with the results of the neural network and the Gumbel method. The algorithms perform less accurately in regions with limited training data, emphasizing the need for additional variables to enhance prediction accuracy.   Research implications: The most significant implication of this research is the potential improvement in intense rainfall forecast, using advanced machine learning techniques such as Random Forests and Deep Learning. Society will benefit with it resulting in better systems, in specific municipalities within the state of Rio de Janeiro, for early warning, risk managements, and urban planning.