This study develops a landslide susceptibility map using a data-driven approach and taking the district Muzaffarabad, northwest Himalayas, Pakistan, as a case study. The Muzaffarabad district was severely affected by the 2005 Kashmir earthquake triggered landslides. To manage these landslides, it is necessary to collect data and information about landslides of the Muzaffarabad district. Therefore, a landslide inventory was prepared by using remote sensing imageries, and 459 landslides were identified. These landslides were divided into training and validation samples ($70\text{:}30\%$ ). About 321 known landslide points were used as training sites in combination of nine causative factor rasters to produce a landslide susceptibility map of the area. About 20% of all training sites were verified through field mapping, and an accuracy of 77% was achieved. The selected causative layers include landcover, lithology, slope angle, elevation, distance to drainage, distance to faults, distance to roads, aspect, and slope curvature. The landslide susceptibility map was classified into low, moderate, high, and very high susceptible zones. The validity of the model was assessed with a success rate curve (SRC), whereas prediction efficiency was analyzed through a prediction rate curve (PRC). The validation results reveal fine compatibility between landslides and the obtained posterior probability model of the area. The efficiency of classification is 89%, and efficiency of prediction is 86.2%. To generate posterior probability, the cumulative area posterior probability (CAPP) curve was used to reclassify the continuous posterior probability scale map into four classes. According to the results of weights calculated through Arc-SDM, landcover, lithology, and slope angle are the three key parameters to determine the occurrences of landslides in the study area. To implement the landslide management strategies, the developed landslide susceptibility map can be used by the concerned agencies.

Landslides are one of the most recurring and damaging natural hazards worldwide, with rising impacts on communities, infrastructure, and the environment. Landslide hazard, vulnerability, and risk assessments are critical for landslide mitigation, land use and developmental planning. They are, however, often lacking in complex and data-poor regions. This study proposes an integrated approach to evaluate landslide hazard, vulnerability, and risk using a range of freely available geospatial data and semi-quantitative techniques for one of the most landslide-prone areas in the Hindukush mountain ranges of northern Pakistan. Very high-resolution satellite images and their spectral characteristics are utilized to develop a comprehensive landslide inventory and predisposing factors using bi-variate models to develop a landslide susceptibility map. This is subsequently integrated with landslide-triggering factors to derive a Landslide Hazard Index map. A geospatial database of the element-at-risk data is developed from the acquired remote sensing data and extensive field surveys. It contains the building’s footprints, accompanied by typological data, road network, population, and land cover. Subsequently, it is analyzed using a spatial multi-criteria evaluation technique for vulnerability assessment and further applied in a semi-quantitative technique for risk assessment in relative risk classes. The landslide risk assessment map is classified into five classes, i.e., very low (18%), low (39.4%), moderate (26.3%), high (13.3%), and very high (3%). The developed landslide risk index map shall assist in highlighting the landslide risk hotspots and their subsequent mitigation and risk reduction.