Landslides are highly destructive geological processes characterized by the rapid movement of rock, soil, or debris down a slope, causing extensive damage to infrastructure and posing a severe threat to human life. Pakistan is prone to natural hazards, including landslides, like other countries, due to its high precipitation, geological setting, and rugged terrain with steep slopes. The foremost objective of landslide susceptibility analysis is identifying hazardous and high-risk areas, followed by appropriate actions to reduce negative impacts from landslides.
Landslide risk analysis in Pakistan involves a combination of field surveys, expert knowledge, and limited use of remote sensing and GIS technologies. However, these methods need help with scalability, objectivity, and data availability. However, our approach will utilize satellite data, topographic maps, field data, and other informative maps as inputs to create a comprehensive and accurate dataset. This will contribute to the field by creating the first-ever public and novel dataset for landslide susceptibility mapping in Pakistan.
We will analyze important terrain factors and generate corresponding thematic data layers representing geological, topographical, and hydrological conditions. We will develop a rating scheme for spatial analysis within a GIS framework, resulting in a landslide susceptibility map with multiple relative susceptibility classes. Hence, we aim to enhance the accuracy, scalability, and objectivity of landslide susceptibility mapping, ultimately mitigating the risks associated with landslides and promoting community resilience in the affected regions of Pakistan.
The occurrence of landslides in the northern areas of Pakistan poses a significant threat to lives and infrastructure. The rugged terrain, high precipitation, and geological characteristics make the region highly susceptible to landslides. Therefore, there is an urgent need for effective landslide susceptibility mapping (LSM) to identify high-risk areas, mitigate the impact of landslides, and enhance community safety and resilience.
Machine Learning and Visualization