Abstract
Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study was implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility in Zhaoping County. To this end, 10 landslide disaster-related variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were provided. Of 345 landslide disaster locations found, 70% were used to train the models, and the rest of them were performed for model verification. The aforementioned four models were run, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, statistical analysis, and field investigation were performed to test and verify the efficiency of these models. Analysis and comparison of the results denoted that all four landslide models performed well for the landslide susceptibility evaluation as indicated by the area under curve (AUC) values of ROC curves from 0.863 to 0.934. Among them, it has been shown that the PSO-RF model has the highest accuracy in comparison to other landslide models, followed by the PSO-SVM model, the RF model, and the SVM model. Moreover, the results also showed that the PSO algorithm has a good effect on SVM and RF models. Furthermore, the landslide models devolved in the present study are promising methods that could be transferred to other regions for landslide susceptibility evaluation. In addition, the evaluation results can provide suggestions for disaster reduction and prevention in Zhaoping County of eastern Guangxi.
Highlights
IntroductionThe geological environment in eastern Guangxi is fragile and landslide disasters occur frequently, which causes huge economic losses and ecological damage, and seriously restricts the survival of human beings and the sustainable development of human society [1,2,3]
Using the natural breaks classification method, the landslide susceptibility of Zhaoping was divided into five levels from low to high: extremely low, low, medium, high and extremely high, as shown in Figure 4 shows that the extremely high susceptibility level for landslides is mainly distributed in the clastic rock areas along the Guijiang River and its tributaries, and the closer the riverbank, the higher its susceptibility index
The Normalized differential vegetation index (NDVI) map of these regions indicates that the vegetation coverage is low, which indirectly reflects the frequent human engineering activities in the regions, indicating that the human engineering construction strongly interferes with the geological ecological environment of the region and leads to the frequent occurrence of landslides
Summary
The geological environment in eastern Guangxi is fragile and landslide disasters occur frequently, which causes huge economic losses and ecological damage, and seriously restricts the survival of human beings and the sustainable development of human society [1,2,3]. With the rapid development of the economy in recent decades, the frequency and intensity of landslide disasters are rapidly increasing with the over-exploitation and utilization of natural resources by humans [4]. It is of great significance to objectively evaluate landslide susceptibility for the reduction and prevention of disasters. Over the past few decades, the most commonly used methods for ascertaining landslide susceptibility in a specific region can be divided into two categories: knowledge-
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