Every monsoon, the Indian Himalayan Region faces the growing threat of landslides triggered by intense rainfall. These disasters often lead to widespread destruction, claiming lives, damaging infrastructure, and disrupting livelihoods. As climate change continues to increase the frequency of extreme weather events, the need for reliable forecasting tools has never been greater.
Addressing this challenge, researchers at the Indian Institute of Technology (IIT) Mandi have developed a fully operational Landslide Early Warning System (LEWS) capable of forecasting landslide risks across the Indian Himalayan Region. By combining satellite observations, machine learning, and real-time rainfall analysis, the system provides daily forecasts that can help authorities and communities prepare before disasters strike.
The research was led by Prof. Dericks Praise Shukla from the School of Civil and Environmental Engineering at IIT Mandi, along with research scholars Ankit Singh and Nitesh Dhiman.
Turning Scientific Data into Life-Saving Warnings
A Landslide Early Warning System is designed to estimate the probability of landslides by analysing terrain characteristics alongside real-time rainfall information. The forecasts enable disaster management agencies to identify high-risk areas, plan evacuations, and implement precautionary measures before conditions become dangerous.
Highlighting the significance of the initiative, Prof. Dericks Praise Shukla said that the system begins issuing daily landslide forecasts at the onset of the monsoon through a dedicated web-based application. By identifying vulnerable areas in advance, it enables authorities and local communities to strengthen preparedness and carry out timely evacuations when necessary.
He added that satellite-based early warning systems represent one of the most effective investments in disaster risk reduction because they convert complex scientific information into actionable insights. A forecasting platform that covers the entire Himalayan region, he noted, can significantly improve preparedness, accelerate emergency response, and enhance coordination among disaster management agencies during the monsoon season.
One of India’s Most Comprehensive Landslide Forecasting Platforms
Unlike several existing landslide warning systems that are limited to specific districts or states, IIT Mandi’s LEWS has been designed to cover the entire Indian Himalayan Region, making it one of the country’s most extensive operational forecasting systems.
The researchers developed the platform through a multi-stage process. They first analysed nearly 26,000 recorded landslides from the Geological Survey of India (GSI) database to prepare a detailed landslide susceptibility map. Multiple environmental and terrain-related factors that influence landslides were integrated using ensemble machine learning models to improve prediction accuracy.
The team then created the Probability of Rainfall-Induced Landslides (P-RIL) model using data from the NASA Global Landslide Catalogue and seven rainfall parameters obtained from IMERG satellite datasets. Since rainfall patterns change continuously, the model dynamically analyses rainfall received over the previous 15 days to estimate landslide probability.
The final daily forecast is generated by combining the static susceptibility map with the dynamic rainfall model through probabilistic analysis. To make the results easier for users to understand, forecasts are categorised into percentile-based risk levels ranging from lower to higher risk.
Accessible Forecasts Through a Digital Platform
To ensure that forecasts reach stakeholders quickly and efficiently, the IIT Mandi team has also developed a Google Earth Engine (GEE)-based web portal. The platform allows users to access landslide forecasts for the current day as well as the previous three days.
Users can also download daily forecast bulletins in PDF format and subscribe to WhatsApp alerts for selected locations, making the system particularly useful for district administrations, disaster response agencies, and local authorities responsible for public safety.
A Step Towards Climate-Resilient Communities
The increasing frequency of extreme rainfall events due to climate change has made landslide forecasting an essential component of disaster risk management. Early warning systems not only save lives by enabling timely evacuations but also help minimise economic losses by protecting infrastructure and improving emergency planning.
The researchers believe that the operational Landslide Early Warning System will significantly strengthen disaster preparedness and risk reduction efforts across the Indian Himalayan Region by delivering timely, location-specific warnings. As climate-related disasters become more frequent, innovations like IIT Mandi’s LEWS demonstrate how satellite technology, geospatial science, and artificial intelligence can work together to build safer and more resilient communities.
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