## 5 Case Study of Landslide Prone Regions
### China  
The findings of this research demonstrate that empirical techniques may be used to model rainfall-induced landslides that frequently occur in China [106]. This may be regarded as a meaningful feature used in systematic landslide modeling, and has the potential to be used in the study of Earth system science for the activation of suitable landslide susceptibility mitigation strategies [107]. Machine learning is a cutting-edge analytic method [108; 109; 110]. It has been extensively employed in the prevention of landslides. Combining a modeling approach with a locally-based mathematical model as well as with other types of remote sensing data is referred to as a combining econometric model [111]. Several similar numerical simulations have been used to study the collapse stages of a Yindongziogu landslide during rainy circumstances. The collapse of the hills has been categorized into three forms based on the degree of the failure: (1) delayed retrogressive toe sliding; (2) numerous retrogressive rolling; and (3) rapid diffusion flow sliding as a whole. Landslide susceptibility mapping using the data cost method was implemented through research into historical landslides, slope monitoring, and statistical analysis, to develop an estimate of the likelihood of landslides occurring for each sensitivity and specificity class, with precipitation expected in the next 24 h being the most important factor [112]. The innovative approach for spatiotemporal forecasting on the basis of grading and overlapped physical parameters, as well as the influence of the rainfall factor, has been used in the analysis of the formation circumstances of rainfall-induced landslides [113]. China is divided into seven main regions and twenty-eight warning regions based on geological and geographical factors. The prediction and alert criteria for each location are created using a statistical study of the quantity and method of rainfall in the 15 days before the landslide occurrence [114]. China is one of the nations that has suffered significant casualties as a consequence of landslides [5]. Annually, landslides claim a large number of lives in China. Climate change is predicted to boost the incidence and magnitude of intense precipitation, resulting in a shift in the incidence of landslides [115]. The purpose of this research was to examine the effects of climate change on the features of the occurrence rainfall that are typically associated with the landslide hazard in China [116]. However, no cohesive equation can presently characterize the I-D threshold in China, owing partly to the absence of ground rainfall gauges in so many places, particularly in the country's west [117]. Due to the possibility of inconsistent seasonal rainfall, the spatio-temporal rainfall patterns derived by interpolation techniques are erroneous [118]. Several studies have demonstrated that the solutions can accurately reproduce the geographical and temporal pattern of actual rainfall, as evidenced by their pathways linking with rainfall gauges in various parts of China [119]. The purpose of this project was to determine if it was feasible to map landslide susceptibility at the national level for China, using limited landslide data and mixed effects modeling [120; 121]. To accomplish this, three leading landslide susceptibility models for China were provided. Each model focused on a false strategy for dealing with the inevitability of regional landslide data insufficiency [120; 122]. It was demonstrated that certain biases may significantly affect the evidential support of future statistical key metrics, as well as the believability of the generated spatial variation [123]. The modeling findings demonstrated that a generalized additive mixed model (GAMM) may be used to mitigate the undesirable impacts of incomplete landslide data [124].