Análisis Comparativo de Estrategias de Pronóstico de Sequías Meteorológicas: De la Estadística Clásica a los Algoritmos de Inteligencia Artificial
DOI:
https://doi.org/10.70577/0dpbb154Palabras clave:
Sequías meteorológicas, Índice Estandarizado de Precipitación (SPI), Pronóstico de sequías, Aprendizaje automático, Modelos de pronóstico, Cuenca del río Paute, Escasez de datos.Resumen
El pronóstico de sequías meteorológicas en cuencas tropicales de montaña se ve limitado a la complejidad orográfica, variabilidad climática y a la escasez de datos. El presente estudio compara cinco modelos de pronóstico (ARIMA/SARIMA, ETS, Random Forest, XGBoost y SVM-RBF) aplicados al Índice Estandarizado de Precipitación (SPI) en escalas de 1, 3, 6 y 12 meses, utilizando registros mensuales de precipitación (1970–2023) de dos estaciones de la cuenca alta del río Paute al sur del Ecuador.
Los modelos fueron calibrados y validados mediante una división 80/20 de entrenamiento-prueba y evaluados con diferentes métricas de desempeño, así como con diferentes pruebas estadísticas. Los resultados indican que la escala temporal del índice SPI fue el principal factor que condiciona el desempeño predictivo en los modelos. Los modelos de aprendizaje automático superaron a los modelos estadísticos clásicos desde SPI-3 a SPI-12, sin embargo, en evaluaciones de pronóstico móvil en el SPI-12, los cinco modelos no presentaron diferencias significativas. La complejidad del modelo no garantizó mejoras en el pronóstico, por lo que se destaca la utilidad de modelos parsimoniosos para el monitoreo operativo de sequías en cuencas andinas las cuales enfrentan escasez de datos.
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Derechos de autor 2026 Darío Xavier Zhiña , Andrea Hernández Allauca (Autor/a)
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