## 1 Introduction  
The landmark movement is the physical process in downhill geomorphic regions, which causes rolls of rubble, regoliths, and large masses of soil in down slope direction under the impact of gravity. It is a predominantly geological event which takes place when the force of the material exceeds the resistance of the shear strength of the soil [1]. Landslides are ranked as the seventh most destructive geohazard among the various natural hazards prevalent around the world in terms of the magnitude of the loss of lives and properties [2]. The occurrence of landslides depends upon the physiographic, hydrological, geological, and geomorphic setups of the study area. Sometimes, the movement of a large landmass in the hilly regions causes a change in topography, drainage pattern, and courses of rivers. The impact of landslides can be attributed to a wide range of phenomenon such as deforestation, habitat loss of human and wild lives, soil loss, disruption of roads and constructions, and flooding in specific cases [3,4]. In the mountainous landscape, landslides occur frequently, triggered by a heavy rainstorm along with interactions among the various environmental causative factors. According to [PERSON] and [PERSON] [5], around 0.49 million sq.km land of India is under the threat of landslide hazard, which is about 15% of the land area of the country. In Indian sub-continent, landmass movement and failure of slope are mostly activated by a heavy precipitation during monsoon, however in the Himalayan belt, the occurrence of landfalls are pretty normal due to seismic activities viz. the Uttarakhand earthquake in 2017, the Chamoli earthquake in 1999, and the Sikkim earthquake in 2011 [6, 7, 8, 9]. [PERSON] [10] mentioned that the mountainous region of Konkan and the Nilgiri ranges in the south and south-western India and the Western Ghats are severely vulnerable to landslides and mass movements. Other than seismic tremors and rainstorm, human interventions and unplanned encroachment, i.e., construction of roads, civil structures etc., disturb the resistance of landmass and accelerate the rate of landslide occurrence. Therefore, it is important to know the spatial relationship between the geo-environmental factors and landslide events for quantitative evaluation of landslide susceptibility (LSS). The reliability of analysis and modelling is determined by the quality and availability of input data and methodology employed. Assembling landslide data in mountainous regions is a very challenging task, mostly owing to poor accessibility. Obtaining data through field inspection techniques can be time intensive and costly, and application of remotely sensed data in this regard is fruitful. The literatures suggested that landslide susceptibility zonation has been performed previously using various bivariate and multivariate statistical techniques, such as frequency ratio [11,12], evidential belief function [13,14], statistical index model [15], logistic regression [12,16], weight-of-evidence [15,17], etc. These studies have shown a good accuracy of these techniques in predicting LSS spatially. However, with the advancement of the machine learning-based modelling, LSS has come out as a greater efficient and effective approach in recent decades. There are various studies that used machine learning and artificial intelligence models in performing landslide susceptibility (LSS) assessment such as random forest [18,19], boosted regression tree [12], naive bayes [20], decision tree [21], neuro-fuzzy [14], artificial neural network [22], support vector machine [20], and many others. However, one of the major weaknesses of statistical models lies in the fact that some assumptions should be defined before conducting any analysis, and therefore a spatial relationship between causative factors is widely neglected [23].