Abstract
The purposes of this study are to identify the maximum number of correlated factors for landslide susceptibility mapping and to evaluate landslide susceptibility at Sihjhong river catchment in the southern Taiwan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN). The landslide inventory data of the Central Geological Survey (CGS, MOEA) in 2004-2014 and two digital elevation model (DEM) datasets including a 5-meter LiDAR DEM and a 30-meter Aster DEM were prepared. We collected thirteen possible landslide-conditioning factors. Considering the multi-collinearity and factor redundancy, we applied the CF approach to optimize these thirteen conditioning factors. We hypothesize that if the CF values of the thematic factor layers are positive, it implies that these conditioning factors have a positive relationship with the landslide occurrence. Therefore, based on this assumption and positive CF values, seven conditioning factors including slope angle, slope aspect, elevation, terrain roughness index (TRI), terrain position index (TPI), total curvature, and lithology have been selected for further analysis. The results showed that the optimized-factors model provides a better accuracy for predicting landslide susceptibility in the study area. In conclusion, the optimized-factors model is suggested for selecting relative factors of landslide occurrence.
Highlights
Landslide susceptibility is defined as the likelihood of a landslide occurring in an area based on local terrain conditions
Landslide susceptibility assessment has attracted the attention of many scholars, and numerous studies have been undertaken for Landslide susceptibility mapping (LSM) assessment around the world (Aksoy and Ercanoglu, 2012; Feizizadeh et al, 2014; Park et al, 2012; Tien Bui et al, 2012)
The certainty factor (CF) values indicate that different spatial resolution of terrain data will not affect the LSM analysis except for the effects on the curvature factors, including total, profile, and plan curvature
Summary
Landslide susceptibility is defined as the likelihood of a landslide occurring in an area based on local terrain conditions. Landslide susceptibility mapping (LSM) plays an important role in hazard mitigation and is an important basis for providing a measure aimed at decreasing the risks associated with landslides (Dou et al, 2014). This involves finding where the risk of landslide-related problems is spatially located, and quantitatively and qualitatively assessing the significance of any such hazards and associated risk factors. A majority of the research has been based on establishing the relationship between the landslide-conditioning factors and landslide occurrence through spatial data analysis These relationships can be characterized in terms of ratings or weight. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN)
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