The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 533–538, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-533-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 533–538, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-533-2020

  06 Nov 2020

06 Nov 2020

METHODOLOGY FOR ESTIMATING LANDSLIDES SUSCEPTIBILITY USING ARTIFICIAL NEURAL NETWORKS

E. N. Muñoz, O. D. Sánchez, and F. L. Hernandez E. N. Muñoz et al.
  • Remote Sensing Research Group, Universidad del Valle, Cali, Colombia

Keywords: Mass Removal Phenomena, Artificial Neural Network, Interferometry, Sentinel 1B, GIS, Remote Sensing, DEM

Abstract. In this study, the susceptibility to landslides at Sevilla township, Valle del Cauca, located at southwest of Colombia was evaluated. The conditioning factors that involve the generation of landslides were evaluated using Geographic Information Systems (GIS) and Remote Sensing (RS) techniques. For the estimating susceptibility, an Artificial Neural Network (ANN) was implemented by applying the “Backpropagation” method to extract the synoptic weights of the conditioning variables (slopes, flow length, curvature, geology, fracture density, and land cover) on an automatic way with a data training module. The data for the analysis of the conditioning factors were carried out through a Digital Elevation Model (DEM) obtained through Radar Interferometry techniques, with Sentinel-1B satellite images for the year 2018. The results showed that Sevilla’s township has areas with high susceptibility, high slopes, and that it’s crossed by an active geological fault which implies that the earth's dynamics will condition the terrain stability.