Semi-quantitative landslide risk assessment of district Muzaffarabad, northwestern Himalayas, Pakistan

The southwestern foothills of the Himalayan Mountain range have been experiencing a surge of catastrophic landslides in the last two decades, as a tragic result of the adverse effects of climate change. This research is about the landslide risk assessment (LRA) which has not been explored yet in the landslide-prone district Muzaffarabad, Pakistan. Landslide susceptibility (spatial probability) was analyzed using random forest model while landslide hazard (temporal probability) was analyzed using Poisson probability model. A random forest-based landslide susceptibility map depicts an accuracy of 0.90. A landslide hazard map was generated by multiplying the temporal probability with the spatial probability and classified as well. Semi-quantitative danger pixels and a fuzzy set theory approach for LRA have been adopted to estimate future landslide risks in the region. The pixel-based LRA approach indicates that 14, 18 and 20 km(2) area of settlement while, the fuzzy set theory-based approach depicts that 15, 19 and 21 km(2) area of the settlement are under very high landslide risk for 1-, 3-, and 5- year return period respectively. Both approaches produced risk maps that designated various risk zones with almost the same area coverage and results. The LRA maps were classified into five classes including very high (1.99%, 2.33%, 2.80%), high (2.16%, 2.53%, 3.04%), moderate (8.02%, 9.79%, 11.22%), low (17.76%, 22.94%, 23.20%), and very low (70.08%, 62.40%, 59.74%) risk zones for 1, 3 and 5 years return period respectively. This research will assist planners and scientists in developing high-precision management strategies for landslide-affected natural resources, especially in the context of the increasing impact of geomorphic hazards on climate change.

Automated summary

This research focuses on the landslide risk assessment in the landslide-prone district of Muzaffarabad, Pakistan. Random forest and Poisson probability models were used to analyze landslide susceptibility and hazard, generating risk maps for the region. The study shows that the area has large high-risk zones for landslides, and both approaches yield similar results.