To compare the random forest (RF) model and the frequency ratio (FR) model for landslide susceptibility mapping (LSM), this research selected Yunyang Country as the study area for its frequent natural disasters; especially landslides. A landslide inventory was built by historical records; satellite images; and extensive field surveys. Subsequently; a geospatial database was established based on 987 historical landslides in the study area. Then; all the landslides were randomly divided into two datasets: 70% of them were used as the training dataset and 30% as the test dataset. Furthermore; under five primary conditioning factors (i.e., topography factors; geological factors; environmental factors; human engineering activities; and triggering factors), 22 secondary conditioning factors were selected to form an evaluation factor library for analyzing the landslide susceptibility. On this basis; the RF model training and the FR model mathematical analysis were performed; and the established models were used for the landslide susceptibility simulation in the entire area of Yunyang County. Next; based on the analysis results; the susceptibility maps were divided into five classes: very low; low; medium; high; and very high. In addition; the importance of conditioning factors was ranked and the influence of landslides was explored by using the RF model. The area under the curve (AUC) value of receiver operating characteristic (ROC) curve; precision; accuracy; and recall ratio were used to analyze the predictive ability of the above two LSM models. The results indicated a difference in the performances between the two models. The RF model (AUC = 0.988) performed better than the FR model (AUC = 0.716). Moreover; compared with the FR model; the RF model showed a higher coincidence degree between the areas in the high and the very low susceptibility classes; on the one hand; and the geographical spatial distribution of historical landslides; on the other hand. Therefore; it was concluded that the RF model was more suitable for landslide susceptibility evaluation in Yunyang County; because of its significant model performance; reliability; and stability. The outcome also provided a theoretical basis for application of machine learning techniques (e.g., RF) in landslide prevention; mitigation; and urban planning; so as to deliver an adequate response to the increasing demand for effective and low-cost tools in landslide susceptibility assessments.

Landslides are one of the most severe and common geological hazards in the world. The purpose of this research is to establish a coupled landslide warning model based on random forest susceptibility zoning and precipitation. The 1520 landslide events in Fengjie County, Chongqing, China, before 2016 are taken as research cases. We adapt the random forest model to build a landslide susceptibility model. The antecedent effective precipitation model, based on the fractal relationship, is used to calculate the antecedent effective precipitation in the 10 days before the landslide event. Based on different susceptibility zones, the effective precipitation corresponding to different cumulative frequencies is counted as the threshold, and the threshold is adjusted according to the fitted curve. Finally, according to the daily precipitation, the rain warning levels in susceptibility zones are further adjusted, and the final prewarning model of the susceptibility zoning and precipitation coupling is obtained. The results show that the random forest model has good prediction ability for landslide susceptibility zoning, and the precipitation warning model that couples landslide susceptibility, antecedent effective precipitation, and the daily precipitation threshold has high early warning ability. At the same time, it was found that the precipitation warning model coupled with antecedent effective precipitation and the daily precipitation threshold has more accurate precipitation warning ability than the precipitation warning model coupled with the antecedent effective precipitation only; the coupling of the two can complement each other to better characterize the occurrence of landslides triggered by rainfall. The proposed coupled landslide early warning model based on random forest susceptibility and rainfall inducing factors can provide scientific guidance for landslide early warning and prediction, and improve the manageability of landslide risk.

This paper constructed the tourism and eco-environment index system, calculated the coupling and coordination relationship between them in Chongqing, from 2000 to 2017, by comparing 9 combination models; and identified the main obstacles through the obstacle model. The results showed that: (1) The comprehensive weighting method (CWM) has the highest accuracy, the coupling coordination mode (CCDM) is more adaptable; the CWM-CCDM is the most suitable. (2) The coupling degree changed from antagonism to run-in phase. (3) The coupling coordination degree improved annually, is in moderate coordination stage currently. (4) The tourism had greater obstacles; the main obstacles vary with time.

To evaluate the long-term security of water resources in Guizhou, this paper presents an evaluation index that incorporates the driving force–pressure–state–impact–response–management (DPSIRM) framework, the gray correlation method, and matter–element analysis. For the period of 2005–2012, our results show that water resources were within the “generally safe” limits for all years except 2006 and 2011, which were characterized by drought conditions. In karst regions, drought has a relatively large impact on water security and is compounded in Guizhou by rapid economic development, nonpoint-source agricultural pollution, and inadequate sewage treatment. Nonetheless, recent efforts to return farmland to forest and grassland and to control desertification have led to increased forest cover and higher levels of soil and water conservation, while systems have been implemented to foster the effective management of water resources in karst areas. In this study, we used both gray matter–element analysis and a DPSIRM framework to assess the state of water resources in Guizhou, the results of which were compared and verified by the gray set pair method, and to provide a reference for evaluating other karst areas.