The increasing frequency of natural hazards, intensified by climate change, poses substantial challenges to sustainable development worldwide. Northern Pakistan, particularly the Hunza district, is highly susceptible to multiple hazards, including landslides, earthquakes, glacier-induced floods, debris flows, and Glacier Lake Outburst Floods (GLOFs), driven by both climatic and tectonic factors. A multi-hazard assessment is essential to understand the complex interactions between these hazards, offering a comprehensive perspective on risk and facilitating more effective disaster preparedness and mitigation strategies. This study addresses the existing gap in multi-hazard assessments, which are often confined to single-hazard evaluations, by developing an integrated multi-hazard susceptibility map for the Hunza district in Northern Pakistan. The region’s complex topography, active tectonics, and accelerated glacier melting contribute to its high vulnerability to cascading and co-occurring hazards. The integrated assessment utilizes diverse data sources, including topographic attributes, geological, hydro-meteorological, environmental variables, and literature-derived hazard map for multi-hazard susceptibility analysis. A Machine Learning (ML) Forest-Based Classification and Regression (FBCR) model, Analytical Hierarchy Process (AHP), and Vs30-based site characterization was employed to classify and generate hazards individually and as integrated multi-hazard susceptibility map. The model incorporates eighteen geo-environmental variables for individual hazards assessment. The resulting multi-hazard susceptibility map indicates that 23.11% of the area is prone to landslides, 6.07% to flash floods, 4.66% to debris flows and flash floods, and 3.98% to a combination of flash floods, landslides, and debris flows. The highest multi-hazard zone, comprising seismic hazard, debris flows, landslides, and flash floods, covers 2.88% of the area, whereas low-hazard zones constitute 56.84% of the region. The landslide susceptibility model classifies 20% of the area as very high susceptible, while the flash flood, debris flow, and seismic hazard models indicate 5, 2, and 13% of the area, respectively, fall under very high susceptibility/hazard. This integrated multi-hazard approach provides a comprehensive risk assessment framework, supporting evidence-based disaster risk reduction policies and infrastructure planning in hazard-prone regions. The findings identify critical high-hazard zones, offering data-driven insights for targeted mitigation strategies and disaster risk reduction efforts.
Khan et al. (Sun,) studied this question.