Landslide Susceptibility Mapping along a Rapidly Uplifting River Valley of the Upper Jinsha River, Southeastern Tibetan Plateau, China

Assessment of environmental geological disaster susceptibility under a multimodel comparison to aid in the sustainable development of the regional economy

Environmental geological disasters pose a significant threat to human life, property and environmental safety. It is necessary to conduct targeted governance in key prevention and control areas based on reasonable susceptibility assessment. Using the debris flow disaster in Xiuyan County as an example, this study compares and analyzes prone prediction models such as the frequency ratio (FR), decision tree (DT) and random forest (FR) models and evaluates the cost of prevention and control and the protection of life and property. The research results show that the FR, DT and RF models have good performance. The ROC test, disaster point density statistics and cross-validation results show that the RF model has the best performance. The study area was mainly less and mildly prone areas. The highly prone areas are mainly distributed in the northeast and southwest of the study area. It is the key area of disaster prevention and control. Elevation, rainfall intensity and population density have the largest influence on the susceptibility to debris flows. Based on the RF model, the disaster points in the highly prone areas account for 54% of the disaster points of the whole area, and the project treatment cost of the disaster points is 0.78 million yuan per single gully, which protects 56% of the lives and property in the study area, which is better than the DT and FR models. The RF model not only has good prediction performance in terms of susceptibility. It can realize the targeted management of disasters, achieve the targeted investment of governance costs and the effective protection of life and property and serve the sustainable development of the regional environment and economy with greater value.

Susceptibility Mapping of Typical Geological Hazards in Helong City Affected by Volcanic Activity of Changbai Mountain, Northeastern China

The purpose of this paper was to produce the geological hazard-susceptibility map for the Changbai Mountain area affected by volcanic activity. First, 159 landslides and 72 debris flows were mapped in the Helong city are based on the geological disaster investigation and regionalization (1:50,000) project of Helong City. Then, twelve landslide conditioning factors and eleven debris flow conditioning factors were selected as the modeling variables. Among them, the transcendental probability of Changbai Mountain volcanic earthquake greater than VI degrees was used to indicate the relationship between the geological hazard-susceptibility and Changbai Mountain volcanic earthquake occurrence. Furthermore, two machine learning models (SVM and ANN) were introduced to geological hazard-susceptibility modeling. Receiver operating characteristic curve, statistical analysis method, and five-fold cross-validation were used to compare the two models. Based on the modeling results, the SVM model is the better model for both the landslide and debris flow susceptibility mapping. The results show that the areas with low, moderate, high, and very high landslide susceptibility are 31.58%, 33.15%, 17.07%, and 18.19%, respectively; and the areas with low, moderate, high, and very high debris flow susceptibility are 25.63%, 38.19%, 23.47%, and 12.71%, respectively. The high and very high landslide and debris flow susceptibility classes make up 85.54% and 80.55% of the known landslides and debris flow, respectively. Moreover, the very high and high landslide and debris flow susceptibility are mainly distributed in the lower elevation area, and mainly distributed around the cities and towns in Helong City. Consequently, this paper will be a useful guide for the deployment of disaster prevention and mitigation in Helong city, and can also provide some reference for evaluation of landslide susceptibility in other volcanically active areas.

Improved Shallow Landslide Susceptibility Prediction Based on Statistics and Ensemble Learning

Rainfall-induced landslides bring great damage to human life in mountain areas. Landslide susceptibility assessment (LSA) as an essential step toward landslide prevention has attacked a considerate focus for years. However, defining a reliable or accurate susceptibility model remains a challenge although various methods have been applied. The main purpose of this paper is to explore a comprehensive model with high reliability, accuracy, and intelligibility in LSA by combing statistical methods and ensemble learning techniques. Miyun country in Beijing is selected as the study area. Firstly, the dataset containing 370 landslide locations inventories and 13 conditioning factors were collected and non-landslide samples were prepared by clustering analysis. Secondly, random forest (RF), gradient boosting decision tree (GBDT), and adaptive boosting decision tree (Ada-DT) were selected as base learners for the Stacking ensemble method, and these methods were evaluated using measures like area under the curve (AUC). Finally, the Gini index and frequent ratio (FR) were combined to analyze the major conditioning factors. The results indicated that the performance of the Stacking method was enhanced with an AUC value of 0.944 while the basic classifiers also performed well with 0.906, 0.910, and 0.917 for RF, GBDT, and Ada-DT, respectively. Regions with a distance to a stream less than 2000 m, a distance to a road less than 3000 m, and elevation less than 600 m were susceptible to the landslide hazard. The conclusion demonstrates that the performance of LSA desires enhancement and the reliability and intelligibility of a model can be improved by combining binary and multivariate statistical methods.