Assessment of Landslide Vulnerability Using Statistical and Machine Learning Methods in Bageshwar District of Uttarakhand, India

In the Bageshwar district, much of its rainfall comes from convectional thunderstorms, which trigger landslides and affect the physical as well as socio-economic environment. It causes loss of human lives, property damage, hampers the transport and communication system, etc. The present study aims to demonstrate the landslide vulnerability zones in Bageshwar district through the aid of ANN and LR models. For fulfilling the objectives of this study factors like slope, elevation, aspect, curvature, TRI, SPI, TWI, land use/land cover, rainfall, distance from the river, distance from lineament and soil were considered to prepare a landslide susceptibility zone map. For identifying the Socio-Economic vulnerable zones the considerable factors used were population density, female density, marginal cultivators, workers density, and literacy, medical facilities, building type, the distance from road, settlement density and household frequency. For better accuracy of the classification here, we opted machine learning methods like ANN and statistical models such as LR. The Landslide vulnerability which is an ensemble of landslide susceptibility and socio-economic vulnerability reveals that 9.15% (ANN), 7.39% (LR) of the area is very highly vulnerable to landslides and 17.77% (ANN), 18.89% (FR) of the area is out of danger for landslide. Therefore, it is expected that the study will give a new direction to the planners which will assist them to take steps regarding this study matter. Finally, for the evaluation of the opted two models, receiver operating characteristics (ROC) were considered for verifying the outcome of this study. ANN model signifies a high accuracy level indicating 84.06% area under the curve (AUC) and LR model indicates 75.79% AUC.