Aprendizaje automático para la predicción de la precipitación de lluvias en la ciudad de Iquitos - Alegria Rodriguez, Jhonatan Waldemar (2025)
This research paper addresses the improvement of accuracy in precipitation prediction in the city of Iquitos through the use of machine learning techniques. The central problem lies in the ineffectiveness of traditional methods for capturing complex rainfall patterns in the Amazon region. The objectives of the study include developing a predictive model using the RandomForestClassifier algorithm, analyzing historical weather data, and evaluating the accuracy of the model by comparing it with traditional methods. The methodology used is based on the collection and analysis of data provided by SENAMHI, followed by the implementation and validation of the predictive model. The results indicate a significant improvement in the accuracy of the predictions with the proposed model, reaching an accuracy of 0. 91 and an average accuracy of 0. 862 in the cross-validation. The findings suggest that the use of machine learning can overcome the limitations of traditional methods, providing more accurate and useful predictions for resource management and planning in Iquitos.