Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions

Abstract

The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection.

A Novel Approach for Forest Fragmentation Susceptibility Mapping and Assessment: A Case Study from the Indian Himalayan Region

“…Now, the WOE method is widely used in many scientific fields [57,58]. Recently, the WOE method has been widely applied for forest fire occurrence, deforestation susceptibility, and landslide susceptibility mapping [59][60][61][62][63]. We applied the WOE method to produce a forest fragmentation susceptibility map.…”

“…The WOE method is used to model the degree of spatial association among each explanatory variable class and training sample by calculating the positive (W + ) and negative weights (W − ), [56,58,64]. This method can produce a map of the expected probability of occurrence using spatial data, which has been applied by many researchers [57][58][59][60][61][62][63][64][65][66][67]. In this study, we applied the WOE method to obtain a forest fragmentation susceptibility map because it avoids the subjectivity of weights assigned to the causative factors.…”

“…It also shows relative certainty of the posterior probability. To calculate the W std , we first calculate weight for each class of causative factor based on the presence (W + ) or absence (W − ) of patch forests [63][64][65][66][67], which are defined as…”

Integrating Landslide Typology with Weighted Frequency Ratio Model for Landslide Susceptibility Mapping: A Case Study from Lanzhou City of Northwestern China

“…On the other hand, statistical models can also be used to calculate the ranking of importance among categories of each factor, which is critical for understanding the impact of different states of the factor on inducing landslides. Many attempts have been made on this topic by researchers using bivariate or multivariate models, such as the weight of evidence model [30], information value model [31], index of entropy model [32], conditional probability model [33], certainty factor method [34], and evidential belief function [35]. In this study, the weights of different classes of a factor were determined by the frequency ratio (FR) model.…”

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

“…Landslides are a natural disaster where land or rocks can follow along the slopes of the mountain. In most cases, landslides occur with or after heavy rains, causing the soil to become so wet that the weight of the soil mass increases and as a result, the adhesion force between the soil masses decreases [ 1 , 2 , 3 ]. Landslides occur slowly or suddenly.…”