Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China

Abstract

Landslide is a common natural hazard and responsible for extensive damage and losses in mountainous areas. In this study, Longju in the Three Gorges Reservoir area in China was taken as a case study for landslide susceptibility assessment in order to develop effective risk prevention and mitigation strategies. To begin, 202 landslides were identified, including 95 colluvial landslides and 107 rockfalls. Twelve landslide causal factor maps were prepared initially, and the relationship between these factors and each landslide type was analyzed using the information value model. Later, the unimportant factors were selected and eliminated using the information gain ratio technique. The landslide locations were randomly divided into two groups: 70% for training and 30% for verifying. Two machine learning models: the support vector machine (SVM) and artificial neural network (ANN), and a multivariate statistical model: the logistic regression (LR), were applied for landslide susceptibility modeling (LSM) for each type. The LSM index maps, obtained from combining the assessment results of the two landslide types, were classified into five levels. The performance of the LSMs was evaluated using the receiver operating characteristics curve and Friedman test. Results show that the elimination of noise-generating factors and the separated modeling of each landslide type have significantly increased the prediction accuracy. The machine learning models outperformed the multivariate statistical model and SVM model was found ideal for the case study area.

Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

“…As mentioned above, DEM was used to extract topographic related landslide conditioning factors, such as altitude, plan curvature, profile curvature, slope angle, slope aspect, and TWI. Altitude is one of the most frequently used factors in landslide susceptibility mapping [53][54][55]. The main reason is that the altitude can influence the topographic attributes which lead to spatial variability of different landscape processes.…”

Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression

“…The factors used in the studies evaluating landslide Landslide inventory is the basis for LSM. Reliable and accurate landslide inventory data are crucial for LSM [51]. To enhance the reliability and accuracy of landslide inventory maps, a total of three techniques were used in the study: Historical reports, interpretation of aerial photographs, and extensive global positioning system (GPS) field surveys.…”

Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques

“…The distance to roads (AM = 0.173, Bel = 0.313) has similar results. The closer we get to the road, the higher the probability of a landslide [115]. This is easy to understand because road construction can destabilize the slope by breaking the support of the slope foot [116].…”