Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China.

In this work, an effective framework for landslide susceptibility mapping (LSM) is presented by integrating information theory, K-means cluster analysis and statistical models. In general, landslides are triggered by many causative factors at a local scale, and the impact of these factors is closely related to geographic locations and spatial neighborhoods. Based on these facts, the main idea of this research is to group a study area into several clusters to ensure that landslides in each cluster are affected by the same set of selected causative factors. Based on this idea, the proposed predictive method is constructed for accurate LSM at a regional scale by applying a statistical model to each cluster of the study area. Specifically, each causative factor is first classified by the natural breaks method with the optimal number of classes, which is determined by adopting Shannon's entropy index. Then, a certainty factor (CF) for each class of factors is estimated. The selection of the causative factors for each cluster is determined based on the CF values of each factor. Furthermore, the logistic regression model is used as an example of statistical models in each cluster using the selected causative factors for landslide prediction. Finally, a global landslide susceptibility map is obtained by combining the regional maps. Experimental results based on both qualitative and quantitative analysis indicated that the proposed framework can achieve more accurate landslide susceptibility maps when compared to some existing methods, e.g., the proposed framework can achieve an overall prediction accuracy of 91.76%, which is 7.63-11.5% higher than those existing methods. Therefore, the local scale LSM technique is very promising for further improvement of landslide prediction.