Stacking Ensemble Learning with Geomagnetic and Meteorological Features for Atmospheric Electric Field Forecasting
sure, wind speed, precipitation, solar radiation, and ultraviolet index, owing to their influence on atmospheric conductivity, ionization processes, cloud electrification, and charge transport mechanisms. In addition, geo- magnetic field measurements were incorporated because of their relationship w...
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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Ponencia |
| Lenguaje: | inglés |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | Enlace permanente (DOI o Handle) |
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| Sumario: | sure, wind speed, precipitation, solar radiation, and ultraviolet index, owing to their influence on atmospheric conductivity, ionization processes, cloud electrification, and charge transport mechanisms. In addition, geo- magnetic field measurements were incorporated because of their relationship with ionospheric disturbances and space weather variability. The final feature set was further supported through correlation analysis, mul- ticollinearity assessment, and feature importance evaluation within the machine learning framework. Prior to predictive model development, an Exploratory Data Analysis (EDA) will be conducted to identify statistical distributions, temporal patterns, outliers, missing values, and nonlinear relationships among ge- omagnetic and meteorological variables associated with atmospheric electric field variability. The study is conceptually grounded in Faraday's law of electromagnetic induction, which establishes the relationship be- tween temporal variations in magnetic fields and the generation of induced electric fields. The proposed methodology employs a stacking ensemble architecture composed of both regularized linear regression and nonlinear learning models, including Ridge Regression, Elastic Net, Random Forest, XGBoost, and LightGBM. These models are integrated through a meta-model designed to optimize predictive perfor- mance and reduce generalization error. Furthermore, temporal feature engineering techniques, including lag variables and rolling statistics, are incorporated to capture dynamic patterns associated with atmospheric and geomagnetic disturbances. Predictive performance will be evaluated using Time Series Split cross-validation and regression metrics such as the coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Additionally, model interpretability will be assessed using feature importance analysis and SHAP values to identify the relative influence of geomagnetic and meteorological variables on atmospheric electric field behavior. The expected result is to demonstrate that the integration of geomagnetic and meteorological variables through co-learning models significantly improves predictive accuracy compared to independent traditional approaches, contributing to the development of intelligent atmospheric monitoring and forecasting systems for space weather applications. Authors: GODOY, Adán (Centro de Instrumentación Científica, Universidad Adventista de Chile, Chillán, 3780000, Chile); URRA PACHECO, Benjamín A. (Centro de Instrumentación Científica, Universidad Adventista de Chile, Chillán, 3780000, Chile); SAAVEDRA VASCONEZ, Carlos E. (Escuela Profesional de Ingeniería Ambiental, Universidad Peruana Unión, Lima, 150118, Perú); VIGO, Eduardo (Escuela Profesional de Ingeniería Ambiental, Uni- versidad Peruana Unión, Juliaca, 21101, Perú); OVALLE MIRANDA, Elías M. (Centro de Instrumentación Científica, Universidad Adventista de Chile, Chillán, 3780000, Chile); CARRASCO BERNALES, Enrique A. (Centro de Instru- mentación Científica, Universidad Adventista de Chile, Chillán, 3780000, Chile); ROJO SEPÚLVEDA, Enrique D. (Centro de Instrumentación Científica, Universidad Adventista de Chile, Chillán, 3780000, Chile); PÉREZ CARPIO, Jackson E. (Escuela Profesional de Ingeniería Ambiental, Universidad Peruana Unión, Lima, 150118, Perú); Prof. FERNÁNDEZ, Joel H. (Escuela Profesional de Ingeniería Ambiental, Universidad Peruana Unión, Lima, 150118, Perú); Dr SORIA-QUIJAITE, Juan Jesús (Escuela Profesional de Ingeniería Ambiental, Universidad Peruana Unión, Lima, 150118, Perú); Dr BRAVO, Manuel A. (Centro de Instrumentación Científica, Universidad Adventista de Chile, Chillán, 3780000, Chile); POMA PORRAS, Orlando (Escuela Profesional de Ingeniería Ambiental, Universi- dad Peruana Unión, Lima, 150118, Perú); QUISPE, Pedro (Escuela Profesional de Ingeniería Ambiental, Universidad Peruana Unión, Juliaca, 21101, Perú); RAMOS CHIPA, Sulamita M. (Escuela Profesional de Ingeniería Ambiental, Universidad Peruana Unión, Lima, 150118, Perú) Presenters: Dr SORIA-QUIJAITE, Juan Jesús (Escuela Profesional de Ingeniería Ambiental, Universidad Peruana Unión, Lima, 150118, Perú); Dr BRAVO, Manuel A. (Centro de Instrumentación Científica, Universidad Adventista de Chile, Chillán, 3780000, Chile); POMA PORRAS, Orlando (Escuela Profesional de Ingeniería Ambiental, Univer- sidad Peruana Unión, Lima, 150118, Perú) Session Classification: Oral Contributions Track Classification: Machine Learning and AI: Space Weather Forecasting & Operations |
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