LANDSLIDE SUSCEPTIBILITY PREDICTION AND WARNING SYSTEM USING MACHINE LEARNING AND GIS

Abstract

Pakistan is one of the most prone regions to natural disaster such as landslides, floods, and droughts etc. Landslides are one of the most catastrophic events which occur frequently and cause numerous causalities and damage to infrastructure as well. The primary cause of landslides is high-intensity rainfall as we know it is the most focused factor in our research which we consider in predicting landslides. We also consider other influence factors such as elevation, altitude, aspect, plan curvature, profile curvature, distance from the river, roads and faults, lithology, soil, Normalized difference vegetation Index (NDVI), topographical wetness index (TWI) and soil moisture. In our research, we estimate the potential impact of these factors on our future landslide susceptibility. We generate an early warning system which alerts the government and public to take precautionary measures to save lives and maximum damage which can be caused by this hazard. We consider a machine learning model of a Random Forest Classifier to predict landslide susceptibility in Astore District, Gilgit Baltistan, Pakistan. We offer a detailed analysis and visual representation of landslide prone areas. Subsequently, We also highlights low, moderate and high-risk areas based on various factors. Additionally, we provide a customization platform, where user can identify high-risk areas of landslides, based on the provided data sets. The present study makes better and precise prediction and decisions to avoid most of the hazards. Predicting landslides is helpful in preventing most of the damage and helps in making better decisions to avoid major loss in future.