Integrated Risk Assessment of Floods and Landslides in Kohistan, Pakistan

Taliah Sajid, Sakina Khuzema Maimoon, Muhammad Waseem, Shiraz Ahmed, Muhammad Arsalan Khan, Jens Tränckner, Ghufran Ahmed Pasha (), Hossein Hamidifar and Charalampos Skoulikaris
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Taliah Sajid: Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23640, Pakistan
Sakina Khuzema Maimoon: Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23640, Pakistan
Muhammad Waseem: Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23640, Pakistan
Shiraz Ahmed: Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23640, Pakistan
Muhammad Arsalan Khan: Department of Civil Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23640, Pakistan
Jens Tränckner: Faculty of Agriculture and Environmental Sciences, University of Rostock, 18059 Rostock, Germany
Ghufran Ahmed Pasha: Department of Civil Engineering, University of Engineering & Technology, Taxila 47050, Pakistan
Hossein Hamidifar: Department of Hydrology and Hydrodynamics, Institute of Geophysics, Polish Academy of Sciences, 01-452 Warsaw, Poland
Charalampos Skoulikaris: Department of Civil Engineering, School of Engineering, Aristotle University of Thessaloniki, GR54124 Thessaloniki, Greece

Sustainability, 2025, vol. 17, issue 8, 1-23

Abstract: Climate change and global warming have increased the frequency and intensity of natural hazards such as floods, landslides, and avalanches. These hazards not only have significant individual impacts but are also interconnected, often amplifying their destructive effects. Therefore, it is crucial to manage their consequences and ensure that communities and infrastructure are resilient enough to withstand these challenges. Given the limited research assessing the collective impact of natural hazards, particularly in Pakistan, this study investigates the effects of floods and landslides in the Kohistan District of northern Pakistan, an area which is highly vulnerable to such hazards yet minimally studied. Machine learning techniques, including the Analytical Hierarchy Process (AHP) and weighted overlay, along with geographic information systems (GISs) and remote sensing (RS), were employed to analyze the causative factors of these hazards. The resulting flood risk and landslide risk maps were then superimposed to produce an integrated dual-hazard risk assessment. The research findings serve as a foundation for policy-making, offering strategies to reduce risks for all stakeholders, implement adaptive measures for communities, and ensure that future developments are both resilient and sustainable.

Keywords: climate change; flood risk; landslide susceptibility; risk assessment; geospatial analysis; geographic information systems; analytical hierarchy process (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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