Estimación de umbrales y dinámica de inundaciones integrando Random Forest, el índice SDWI de Sentinel-1, el modelo HAND y datos SAR en la cuenca del río Tumbes
Enviado: 31-03-2026
|Aceptado: 06-07-2026
|Publicado: 31-07-2026
Derechos de autor 2026 Ingeniería del Agua
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
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Palabras clave:
Sentinel-1, Random Forest, modelo HAND, Google Earth Engine, río Tumbes
Agencias de apoyo:
Resumen:
El presente trabajo propone un marco metodológico para superar la alta nubosidad y escasa instrumentación en cuencas tropicales, avanzando del mapeo estático a la proyección espacial de inundaciones. Para ello, se integraron imágenes de radar Sentinel-1, el algoritmo Random Forest (RF), el índice SDWI y el modelo topográfico HAND mediante Google Earth Engine. Para cuantificar el área neta inundada, se restó el espejo de agua permanente y se aplicaron máscaras de exclusión. La relación entre el caudal y la superficie afectada se analizó mediante validación cruzada (LOOCV). La evaluación de 13 eventos hidrológicos (2023-2025) indicó que el modelo integrado (RF + SDWI) alcanzó una Exactitud Global del 91.0%, un F1-score de 0.80 y una Intersección sobre Unión (IoU) del 69.0%. Asimismo, el ajuste mediante una regresión polinómica de segundo grado permitió estimar la extensión del desborde con un error (RMSE) de ±0.87 km². Se concluye que esta integración proporciona una herramienta empírica de apoyo para los Sistemas de Alerta Temprana, facilitando una estimación espacial preliminar que contribuye a la gestión del riesgo frente a pronósticos hidrológicos extremos.
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