Globally, natural hazards have become more destructive in recent times because of rapid urban development and exposure. Consequently, significant human life loss, the damage to property and infrastructure, and the collapse of the environment directed the attention of geoscientists to control the consequences and risk management in relation to geo-hazards. In this research, an effort was made to produce a compound map, geo-visualizing the susceptibility of multi-hazards, to select suitable sites for sustainable future development and other economic activities in the region. Muzaffarabad District was chosen as a case research area due to the high magnitude of hydro-meteorological and geological hazards. On the one hand, both selected geo-hazard inventories were developed using the field survey and remote sensing data. The subjective and objective weight of all the causative factors and their classes were calculated using the assembled geospatial techniques, such as the Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) in the Geographic Information System (GIS). The results reveal that the most suitable areas are distributed in the southern and northwestern parts, which can be used for future sustainable development and other economic activities. In contrast, the eastern and western regions, including Muzaffarabad City, are within high and very susceptibility zones. Finally, more than 50% of the land area is located in very low and low susceptibility zones. The validation of the proposed model was checked by using three different techniques: the Receiver Operative Characteristic (ROC) curve, Seed Cell Area Index (SCAI), and Frequency Ratio (FR). Both ROCs, the Success Rate Curve (SRC) and the Predictive Rate Curve (PRC), showed the goodness of fit for both the selected geo-hazards: landslides (81.3%) and floods (93.2%), at 80.1% and 91.7%, respectively. All the validation techniques showed good fitness for both the individual and multi-hazard maps. The proposed model sets a baseline for policy implementation for all the stakeholders to minimize the risk and sustainable future development in areas of high frequent geo-hazards.

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.