Técnicas para la predicción espacial de zonas susceptibles a deslizamientos

• Florez García, Andrés Camilo ^[1] ; Pérez Castillo, José Nelson ^[1]
  1. [1] Universidad distrital francisco jose de caldas
• Localización: Avances: Investigacion en Ingeniería, ISSN-e 2619-6581, ISSN 1794-4953, Vol. 16, Nº. 1, 2019, págs. 20-48
• Idioma: español
• DOI: 10.18041/1794-4953/avances.1.5188
• Títulos paralelos:
  • Spatial prediction Techniques for landslide-prone areas
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Si bien las técnicas implementadas para predecir zonas susceptibles a procesos de remoción en masa han logrado modelar con cierto grado de precisión casos de deslizamientos, no logran modelar eventos complejos, donde la relación entre los deslizamientos y sus factores desencadenantes no presentan un comportamiento lineal. Lo anterior se debe a ausencia de estructuras de dependencia espacio-temporal que permitan evaluar efectos espaciales (autocorrelación y heterogeneidad); por lo tanto, la interpretación de los resultados suele ser errada y lleva a una menor confiabilidad. Dado lo anterior, el objetivo del artículo es brindar un documento sólido que ofrezca una perspectiva general y detallada de las técnicas de predicción espacial; al tiempo que se propone una metodología innovadora que permita utilizar las bondades del aprendizaje automático y la estadística espacial, con el propósito de mejorar el desempeño predictivo de zonas susceptibles a deslizamientos.

  • English

    The implemented techniques for the prediction of landslide-prone areas have been effective at a certain degree. However, many approaches tend to face difficulties to determine non-linear landslides triggering factors, due to the absence of Spatio-temporal dependency structures that evaluate spatial effects as autocorrelation and heterogeneity when describing complex problems. Therefore, results understanding may not be precise and lead to a less reliability condition. The main objective of this article is to provide a solid document that offers both, a general and a detailed perspective about Spatial Prediction Techniques. Finally, we propose an innovative methodology that allows us to use automatic learning and spatial statistics to improve the predictive performance of landslide-prone areas.

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