Nehu develops AI-based landslide susceptibility map for Meghalaya

Shillong: The department of information technology at North-Eastern Hill University (Nehu), Shillong, has developed an AI-based Landslide Susceptibility Map (LSM) of Meghalaya using an ensemble Machine Learning (ML) framework combining 10 different machine learning models to improve the map’s accuracy, robustness and reliability.

Meghalaya’s complex geological structure, frequent seismic activity and intense monsoon rainfall make it highly prone to landslides, causing loss of life and property every year. Experts say the impact can be reduced by identifying vulnerable areas and monitoring them regularly.


The research was carried out by K Amitab and his team with financial support from the Science and Engineering Research Board under the department of science and technology (DST), Govt of India. Historical landslide inventory data from the Geological Survey of India and the North Eastern Space Applications Centre (NESAC) were used to train and evaluate the model.

“The framework achieved an accuracy exceeding 90 per cent, demonstrating its effectiveness in predicting landslide-prone zones. The generated LSM classifies landslide susceptibility of Meghalaya into five risk categories: very high, high, moderate, low, and very low,” a Nehu statement says.

“According to the map, approximately 7% of Meghalaya falls under very high-risk category, while 6%, 8%, 19%, and 60% fall under the high, moderate, low, and very low categories, respectively. The East Khasi Hills district is the most vulnerable region, with approximately 730 kms falling under the very high risk category. Other vulnerable districts include Ri Bhoi, Eastern West Khasi Hills, West Khasi Hills, Southwest Khasi Hills, and East Jaintia Hills and West Jaintia Hills,” the statement elaborates.

“An analysis of landslide causative factors, revealed that proximity to roads is the most influential factor in landslide occurrence. This is attributed to slope destabilization during road construction, alteration of natural drainage patterns, and disturbance caused by vehicle movements. Other influential causative factors include Slope degree, NDVI, soil type, elevation, road density, and lithology,” the release read.

“The LSM can serve as a valuable tool for disaster management agencies in prioritizing resource allocation to high-risk regions and guiding proactive planning to mitigate the impact of landslide. The research marks a significant advancement in improving public safety and reducing landslide-related hazards in Meghalaya,” the statement highlights.