Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 24th Aug 2026, 05:32:23am America, Santiago
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Daily Overview |
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62B: Computer Science Location: Room 02: Aconcagua | |
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10:23am - 10:35am
Precipitation Prediction in Ecuador Using Machine Learning Models Universidad Internacional del Ecuador, Ecuador Precipitation prediction plays a critical role in water resource management, agriculture, and climate risk mitigation, particularly in regions characterized by strong climatic variability, such as Ecuador. This study investigates the application of machine learning techniques to precipitation prediction using a long-term climatic dataset spanning 1950 to 2023. Three regression models were evaluated: Decision Tree Regressor, Random Forest Regressor, and Gradient Boosting Regressor. The dataset was preprocessed through temporal decomposition, logarithmic transformation of precipitation, and cyclical encoding of seasonal effects to capture long-term trends and annual variability. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). Results show the Gradient Boosting Regressor achieving the best performance (MAE = 27.63 mm, RMSE = 37.29 mm, R² = 0.84). Scenario-based predictions and sensitivity analysis further demonstrate the model’s physical consistency and practical applicability. The findings confirm the potential of machine learning models, particularly gradient boosting, as reliable tools for precipitation prediction and exploratory climate analysis in Ecuador. | |