# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 4 Discussion  
The results of the review provide researchers a comprehensive insight to understand the studies being conducted in various avenues of the Indian Himalaya, thus providing a general picture of the state-of-the-art in this region and a reference point for those (researchers and practitioners) starting to work in this area for the first time. Moreover, the analysis of the results can be useful in identifying some existing research problem and the most important research questions to be addressed in the near future.  
The first gap to be urgently filled is the lack of studies in regions such as the Northeast and Jammu and Kashmir Himalayas. Since research programs have focused mainly on Uttarakhand region, which is understandable given the high population density and risk exposure, more efforts need to be made to analyze other regions as well and to help policy makers in formulating a general guideline towards mitigation. Another issue is the urgent need for high quality data, as many of the reviewed studies suffer from the coarse dataset being employed. As an instance, rainfall thresholds have been conducted using a very sparse rain gauge network that measures rainfall only at daily time steps; and most of the susceptibility studies make use of a very coarse geological map at the 1:250,000 scale. It is well established that the poor quality of input data has a negative impact on the quality of the results of any scientific model; therefore, priority should be given to the acquisition of high-resolution basic data for further research.  
In the Indian Himalaya, many studies have been conducted that explore different disciplines of landslide research in various regions, and a precise framework is not evident. One of the next steps of research in the region could be the combination of existing studies in a more comprehensive and coherent framework aimed at assessing and reducing landslide risk. For instance, susceptibility maps could be used to define the most hazardous areas, rainfall thresholds to provide temporal forecasts especially for rapid movements, regional scale remote sensing systems to get a general overview of slow-moving landslides, and ground based monitoring systems could be employed for management of the hot-spots at highest risk level. Similar programs should also consider landslide hazard evolution as influenced by climate change and human modifications to the territorial setting.  
Another significant gap highlighted by the review is that, even though landslides are a well-known source of risk in the area, a quantitative risk assessment has never been carried out, thus all past and present risk reduction strategies are implemented without a comprehensive framework that could optimize the efforts made. At present, landslide hazard studies are also quite limited: examples exist that encompass temporal probability of occurrence (by means of rainfall thresholds) or spatial probability of occurrence (by landslide susceptibility maps). A spatiotemporal hazard assessment would be desirable and, to obtain it the future, two options could be explored: the application of distributed physically-based models (which, in turn, require the acquisition of many input parameters with high spatial density--see, e.g., [PERSON] et al. [111]) or the adoption of simpler approaches based on the dynamic combination of rainfall thresholds and susceptibility maps [112].  
Several techniques have been used to understand various aspects; however, the use of computational techniques has been limited to susceptibility and hazard analysis. It is encouraging to see the use of advanced machine learning techniques (ANN, SVM, and Random Forest) for landslide mitigation. However, the use of deep learning and artificial intelligence, which shows great promise in geohazards and climatic studies, is yet to be performed for the Indian Himalayan region. The use of advanced computational techniques (deep learning) especially for landslide identification is one aspect which researchers should look to exploit.