## 7 Conclusions and Future Recommendations  
This study performed a scientometric evaluation of the susceptibility of landslides in landslide-prone regions. The scientometric analysis method was adopted in order to find the most related research topics, the trends in papers published by authors, the co-occurrence of keywords, and the most active nations in the landslide field. Furthermore, the forecasting and monitoring methods connected with landslides were investigated, as well as the effect of landslides on nations such as China, Italy, India, and the United States. The effect of landslides, as well as various forecasting methods, were specifically studied. The following conclusions are presented:  
Figure 21: Impact of rainfall–induced landslide on quality of water [10]. * A scientometric study of data from the database of Scopus showed that the top three disciplines in counting documents were geology, engineering and environmental research ecology, and meteorology and atmospheric science, accounting for 19%, 18%, and 17% of total papers, respectively. There was a slight increase in the number of articles regarding landslides between 2000 and 2010. Furthermore, statistics gathered between 2014 and 2022 indicated a significant increase in publications. The top four most often appearing words were landslides, rainfall, slope stability, and soils. Furthermore, China, Italy, and Japan contributed the most articles on the subject of rainfall-induced landslides. [PERSON] was the most cited author, with 1652 citations. * Physical models of landslides are used to simulate the process of landslide occurrence in physically based landslide susceptibility assessment techniques. A disadvantage of utilizing a physical model is that good mechanical and hydrological soil characteristic data from natural areas is not always available. In physical models, grid cells are used for analysis which have lower resolution. In the probabilistic approach, if the data covers a long-time span in order to cover a present period, the outcomes are meaningful. The disadvantage of using probabilistic methods is their inventory approach, as it is not workable for multi-temporal landslides. Remote sensing is extremely useful for exploring changes in the surface. If the detection can be repeated with sufficient frequency, the RS technique may be utilized as a monitoring tool. Remote rainfall observations can be used to anticipate rainfall-induced landslides. * There is a need for further study in the Jammu and Kashmir Himalayas and the northeastern zone. The focus has been on states and territories including Uttarakhand, Darjeeling, and parts of Himachal Pradesh. Computational techniques have been shown to be superior to conventional methods in modeling. To understand the region's heterogeneity and unpredictability, regional- to site-specific analysis using hybrid models and big data analytics can be conducted. Rainfall-induced shallow landslides have hit the Italian and Mediterranean hilly regions of Liguria hard; a semi-quantitative method combining predisposing factors and unstructured data was used for landslide prediction in this area. In China and the USA, river water quality, caused by shallow landslides, can be estimated by the portion of the landslide area at the catchment level, which is more flexible than the local structure of a single landslide and a located near flow. * Future research directions that need to be explored are as follows:
* More research is required to establish if areas with high relief and steep terrain, but no recorded landslides, need additional landslide inventory mapping. * Work should be performed to combine remote sensing data, numerical models, and other landslide-related variables. More detailed remote sensing images should be used for landslide monitoring, risk assessment and hazards assessment. * A publicly available approach will encourage continuing contributions to enhance landslide characterization and awareness across nations.