## 4 Discussion  
Since hilly and mountainous areas are more vulnerable to natural dangers, planning and development must be performed cautiously. One of these dangers is the possibility of a landslide. Landsslides are likely to occur under similar conditions as those observed in previous studies [74; 75]. Landsslide susceptibility mappings are crucial in these areas because they provide decision-makers and planners with a better understanding of the process, allowing them to take the first line of defensive action [76]. It is challenging to create an accurate LSM that can be used to designate areas prone to landslides [77]. As a consequence, several solutions are being developed daily all around the globe to handle these challenges of accuracy and reliability [78]. Due to the exhaustive research of LSM, new methodologies have been created. Our research employs MCDA algorithms to develop an accurate model of landslide hazard zonation. LCFs help to initiate landslides. It is critical to choose the appropriate LCFs when developing an accurate landslide susceptibility model. LSM models provide strong predictive capability with less inaccuracy as a consequence. There are numerous LCFs, and they vary depending on the characteristics locally and globally. These landslide indicators are linked to the land use, climatic, geological, and geomorphological variables that regulate landslides. The area's edge is dominated by the convex slope, the middle section by the straight slope, and the bottom portion by the concave slope. There are no standards for selecting LCFs based on the variety of indicators and the characteristics of the region [79]. Much effort has been spent in choosing the most appropriate and stressful factors. The multi-collinearity test helps us to find the correlation between LCFs that may impact the overall accuracy of the models. Ten (10) LCFs were chosen as independent layers in the present research to assess the susceptibility of the area to landslides. The VIF was used to evaluate the LCFs' multi-collinearity. Our findings show that the layers have no multi-collinearity between them. As a result, all variables were included in this model. According to the study's results, one of the reliable approaches for LSM is AHP, which describes the weighted-overlay analysis technique with a multi-criteria decision approach. To classify the area into different susceptibility zones, ten (10) thematic layers were used, including rainfall, slope, earthquake, aspect, lithology, drainage density, lineament density, LULC, soil, and geomorphology. Figure 5 depicts a statistically generated landslide susceptibility map interpreted using the landslide hazard index (LHI) value. The model's min and max LHI values were 1393 and 5391, respectively, with a mean of 2606.88 and a standard deviation (s.d.) of 584.93 (Figure 8). Since this histogram showed that the values were unevenly distributed, the natural-break categorization approach was employed for zonation mapping [80]. As a result, four landslide hazard zones were identified and mapped: low susceptibility, moderate susceptibility, high susceptibility, and extremely high susceptibility (Figure 5). The analytical area % of the extremely high susceptibility zone for AHP is 11.68. (Figure 9). According to the hazard area, AHP falls within the high to extremely high zone with 37.86 percent (Figure 10). As a result, we may assume that the whole study area is in the high-risk zone. The AHP methodology is widely used in landslide susceptibility mapping. However, it has some limitations in the context of the selected area. One limitation of using AHP methodology for landslide susceptibility mapping in Darjeeling is that it relies heavily on expert opinion, which may be subjective and may vary from person to person. The quality and availability of data can also impact the accuracy of the AHP model, especially in areas where data are limited.