Combining neural networks and geostatistics for landslide hazard assessment of San Salvador metropolitan area, El Salvador

Ricardo Ríos; Alexandre Ribó; Roberto Mejía; Giovanni Molina

Ayuda

Combining neural networks and geostatistics for landslide hazard assessment of San Salvador metropolitan area, El Salvador

Autores: Ricardo Ríos, Alexandre Ribó, Roberto Mejía, Giovanni Molina
Localización: Revista de Matemática: Teoría y Aplicaciones, ISSN 2215-3373, ISSN-e 2215-3373, Vol. 23, Nº. 1, 2016, págs. 155-172
Idioma: inglés
DOI: 10.15517/rmta.v23i1.22439
Títulos paralelos:
- Combinando redes neuronales y geoestadística para evaluación de deslizamientos de tierra del área metropolitana de San Salvador, El Salvador
Enlaces
- Texto completo (pdf)
Resumen
- español
  Esta contribución describe la creación de un modelo de evaluación de deslizamiento de tierra para el Área Metropolitana de San Salvador, departamento de El Salvador. El análisis inició con la obtención de una foto aérea del Ministerio de Medio Ambiente y Recursos Naturales (MARN) en donde 4792 deslizamientos fueron identificados y georeferrenciados junto con 7 factores condicionantes incluyendo: geomorfología, geología, precipitaciones máximas, aceleraciones sísmicas, pendiente del terreno, distancia a carretera y falla geológica. Redes Neuronales Artificiales (RNA) fueron utilizadas para la evaluación de la susceptibilidad a deslizamiento de tierra, logrando que más del 80% de deslizamientos fueran apropiadamente clasificados usando un criterio dentro y fuera de la muestra con la que se estimaron los parámetros del modelo. Regresión Logística fue usada como base de comparación, obteniendo este modelo un rendimiento inferior. Para completar el análisis se realizó la interpolación de puntos usando el método kriging proveniente del enfoque geoestadístico. Finalmente, los resultados muestran que es posible obtener un mapa de riesgo a deslizamiento de tierra, haciendo uso de una combinación de RNA y técnicas geoestadísticas con lo cual la presente investigación puede ayudar a la mitigación de deslizamientos de tierra en El Salvador.
- English
  This contribution describes the creation of a landslide hazard assessment model for San Salvador, a department in El Salvador. The analysis started with an aerial photointerpretation from Ministry of Environment and Natural Resources of El Salvador (MARN Spanish acronym), where 4792 landslides were identified and georeferenced along with 7 conditioning factors including: geomorphology, geology, rainfall intensity, peak ground acceleration, slope angle, distance to road, and distance to geological fault. Artificial Neural Networks (ANN) were utilized to assess the susceptibility to landslides, achieving results where more than 80% of landslide were properly classified using in-sample and out of sample criteria. Logistic regression was used as base of comparison. Logistic regression obtained a lower performance. To complete the analysis we have performed interpolation of the points using the kriging method from geostatistical approach. Finally, the results show that is possible to derive a landslide hazard map, making use of a combination of ANNs and geostatistical techniques, thus the present study can help landslide mitigation in El Salvador.
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