Prediction Capability of Bivariate Statistical Model for the Evaluation of Landslide Probability in Sub-humid and Seismic Active Region of Azad Kashmir, Lesser Himalayas
Planning and management are necessary tools throughout the world, especially in a mountainous regions where various natural hazards affect the area socially and economically. Landslides are one of the most common natural hazards in mountainous regions throughout the world. For this purpose, qualitative and quantitative methods and landslide susceptibility mapping were used to reduce the probability of landslide occurrence in an affected area and landslide mitigation. The study was focused on landslide susceptibility mapping in Neelum valley using a relative effect model integrated with 17 causative factors of landslides. These factors such as elevation, slope gradient, slope aspect, general curvature, geology, plan curvature, profile curvature, drainage density, stream power index, distance from stream, distance from road, topographic roughness index, Normalized difference wetness index, distance from fault, rainfall, landuse landcover, and Normalized difference vegetative index were used for analysis. Neelum valley is the part of the Himalayan region that is often experiencing landslide hazards frequently. Among other methods, it is a statistical method prepared within the Geographical information system environment to develop landslide hazard zones in Neelum valley. The landslide inventory map was shown the presence of past landslides in the study area by using Google Earth and remote sensing imageries. Total landslides were 368 in number, 30% (110) for testing purposes and 70% (258) for training purposes. The validation of Relative effect model was calculated with the Area under the curve such as the success rate curve and the prediction rate curve. This was adopted to check the validation and optimum landslide susceptibility zone categorization. The success rate curve of the model was 82.15% calculated whereas 82.73% was the predictive rate curve. According to the study, landslide susceptibility mapping was classified into four classes with 18.14% of the area being very high zone, 34.04% of the area being high zone, 30.26% area being moderately susceptible zone and 17.56% of the area being low susceptible to landslide occurrence zone. Hence, the results of this study highlight the spatial information of the area that may face landslide hazards and may be very helpful to planners, engineers, government agencies, stakeholders and other participants for the prevention, mitigation, management, and monitoring of landslide hazards and this model may also be applicable in other landslide areas.
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