Distribution Modeling and Factor Correlation Analysis of Landslides in the Large Fault Zone of the Western Qinling Mountains: A Machine Learning Algorithm

Abstract

The area comprising the Langma-Baiya fault zone (LBFZ) and the Bailongjiang fault zone (BFZ) in the Western Qinling Mountains in China is characterized by intensive, frequent, multi-type landslide disasters. The spatial distribution of landslides is affected by factors, such as geological structure, landforms, climate and human activities, and the distribution of landslides in turn affects the geomorphology, ecological environment and human activities. Here, we present the results of a detailed landslide inventory of the area, which recorded a total of 2765 landslides. The landslides are divided into three categories according to relative age, area, and type of movement. Sixteen factors related to geological structure, geomorphology, materials composition and human activities were selected and four machine learning algorithms were used to model the spatial distribution of landslides. The aim was to quantitatively evaluate the relationship between the spatial distribution of landslides and the contributing factors. Based on a comparison of model accuracy and the Receiver Operating Characteristic (ROC) curve, RandomForest (RF) (accuracy of 92%, area under the ROC of 0.97) and GradientBoosting (GB) (accuracy of 96%, area under the ROC curve of 0.97) were selected to predict the spatial distribution of unclassified landslides and classified landslides, respectively. The evaluation results reveal the following. The vegetation coverage index (NDVI) (correlation of 0.2, and the same below) and distance to road (DTR) (0.13) had the highest correlations with the distribution of unclassified landslides. NDVI (0.18) and the annual precipitation index (API) (0.14) had the highest correlations with the distribution of landslides of different ages. API (0.16), average slope (AS) (0.14) and NDVI (0.1) had the highest correlations with the landslide distribution on different scales. API (0.28) had the highest correlation with the landslide distribution based on different types of landslide movement.

Graph-Represented Broad Learning System for Landslide Susceptibility Mapping in Alpine-Canyon Region

“…The study area is located in a mountainous area that contains a major streamflow and extensive records of accumulated layer landslides. As we have been investigating the causes of a series of colluvial landslides in the study area in depth, such as the Jiangding Cliff landslide and the Yahuokou landslide in Zhouqu County [18], the landslide and its surrounding areas are always located in the low hills and gullies, with great topographic fluctuations and steep natural slopes. The old landslide area forms a relatively gentle platform, which is conducive to rainwater collection and leakage as well as the formation of landslides.…”

“…However, the estimation and prediction via statistical methods rely on the strong hypothesis of data distribution, which may not hold true for real data. In recent years, machine learning is increasingly being used for the LSM with good results [17,18] attributed to the continuous development of AI technology and the improvement of computational hardware. As one of the generalized linear models, logistic regression (LR) [19][20][21] is a basic binary classifier estimating the possibility of occurrence, which is easy to understand and implement with low computational cost.…”