Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy

• FCM sampling strategy is used to refine the “seed cell”.
• The pre-existing landslides are used to evaluate the performance of the models.
• AUC values are applied to assess the effectiveness of the models.

Several mathematical models are used to predict the spatial distribution characteristics of landslides to mitigate damage caused by landslide disasters. Although some studies have achieved excellent results around the world, few studies take the inter-relationship of the selected points (training points) into account. In this paper, we present the Fuzzy c-means (FCM) algorithm as an optimal method for choosing the appropriate input landslide points as training data. Based on different combinations of the Fuzzy exponent (m) and the number of clusters (c), five groups of sampling points were derived from formal seed cells points and applied to analyze the landslide susceptibility in Mizunami City, Gifu Prefecture, Japan. A logistic regression model is applied to create the models of the relationships between landslide-conditioning factors and landslide occurrence. The pre-existing landslide bodies and the area under the relative operative characteristic (ROC) curve were used to evaluate the performance of all the models with different m and c. The results revealed that Model no. 4 (m=1.9, c=4) and Model no. 5 (m=1.9, c=5) have significantly high classification accuracies, i.e., 90.0%. Moreover, over 30% of the landslide bodies were grouped under the very high susceptibility zone. Otherwise, Model no. 4 and Model no. 5 had higher area under the ROC curve (AUC) values, which were 0.78 and 0.79, respectively. Therefore, Model no. 4 and Model no. 5 offer better model results for landslide susceptibility mapping. Maps derived from Model no. 4 and Model no. 5 would offer the local authorities crucial information for city planning and development.