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

Abstract

Abstract: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.

Environmental controls on Landsat‐derived phenoregions across an East African megatransect

“…The simplicity and efficiency of the K‐means clustering algorithm have resulted in its application across many disciplines (Bradley and Bradley 1998, Hoffman et al 2008, Kumar et al 2011, Mills et al 2011, Senthilnath et al 2017, Wang et al 2017, Pascucci et al 2018). Because K‐means is independent of location, the algorithm can categorize pixels in a Landsat scene that are not spatially close but belong to the same phenoregion (Kumar et al 2011).…”

Remote Sensing of Landslides—A Review

“…Bivariate and multivariate statistical techniques, through the use of Dempster-Shafer weights of evidence and multiple regression methods [17], and techniques that combine principle component analysis and fuzzy membership techniques were used and tested [18]. A physically based model was applied in the rainfall-induced shallow landslide susceptibility analysis because of its ability to reproduce the physical processes governing landslide occurrence [19].…”

“…Multi-temporal assessment of the state of landslide activity was performed based on geomorphological evidence criteria and past ground displacement measurements obtained by InSAR in the basin of Abruzzi, Italy [1]. Landslide susceptibility mapping was presented by integrating information theory, K-means cluster analysis and statistical models in the Three Gorges Area, China [18]. The regional landslide susceptibility and uncertainty assessment in Gangwon Province, Republic of Korea was obtained by fuzzy set theory coupled with the vertex and the point estimate methods [19].…”

Identifying Peat Soil Layers in the South Kalimantan, Indonesia Using K-Means Clustering

“…The number cluster is determined to be accurate if 15 iterations have the convergent centroid obtaining 0 (zero). Determination of the number cluster is done to analyze the data [30]. The iteration is in 15 iterations; however, the convergent criteria obtains 0-13 iterations.…”

“…The cluster K-means method has been applied to analyze topography and soil data [22], water classification [23], transportation [24], electricity consumption [25][26][27], tumor areas in the field of medicine [28], and validate the existence of oil palm plantations [29]. K-means clustering determines the landslide boundary in the Three Gorges area in China [30]. The cluster means method divides the segments based on similar data.…”