Future Estimation of Hydrometeorological Variables Using Machine Learning Techniques: A Comparative Approach

Objective: The objective of the research was to analyze and compare different machine learning models to identify which technique presents the best performance in predicting hydrometeorological variables. Theoretical Framework: This section presents the main concepts that underpin the work. Machine learning techniques such as support vector machines, decision trees, random forests, artificial neural networks, and gradient boosting are presented, providing a solid foundation for understanding the context of the investigation. Method: The study uses a comparative methodology by applying machine learning techniques to predict hydrometeorological variables based on data collected in Petrolina-PE. Various machine learning techniques were employed and compared. Data normalization was performed through logarithms, and the treatment included filling or excluding inconsistent records. The effectiveness of the models is evaluated using metrics such as the Nash-Sutcliffe efficiency coefficient, Willmott index, and Pearson correlation coefficient. Results and Discussion: The obtained results showed good predictability, ranging from 50 to 70% efficiency. The comparative analysis of the results allowed identifying patterns and relationships between variables and initial configurations of the algorithms, contributing to a better understanding of hydrometeorological processes and their predictability. Research Implications: By providing more accurate and reliable forecasts, the models presented can assist managers in making decisions about the sustainable use of water and the mitigation of natural disasters such as floods. Originality/Value: This study contributes to the literature by advancing the estimation of hydrometeorological variables, improving existing techniques, and providing more accurate data for water resource management. Its impact extends from mitigating risks associated with extreme hydrological events to promoting efficiency in the use of water resources, contributing to the sustainability and resilience of aquatic ecosystems, essential in the face of climate change and environmental challenges.