Validation of Spatial Prediction Models for Landslide Susceptibility Maps

A wide range of numerical models and tools have been developed over the last decades to support the decision making process in environmental applications, ranging from physical models to a variety of statistically-based methods. In this study, a landslide susceptibility map of a part of Bailongjiang River, in northwest China was produced, employing binary logistic regression analyses. The available information includes the digital elevation model of the region, geological map and different GIS layers including land cover data obtained from satellite imagery. The landslides were observed and documented during the field studies. To achieve the most appropriate results some sensitivity analyses were also carried out. To validate the quality of mapping, the studied area was divided into training part (3 sub-basins) and validation part (2 sub-basins). Correct classification percentage and Root Mean Square Error (RMSE) values for the validation data for that case were estimated as 76.6% and 0.432, respectively.