IIT Mandi develops landslide warning system for the Himalayas with WhatsApp alerts

Climate change has led to a noticeable rise in landslides across the world, posing an increasing threat to lives, infrastructure and ecosystems. In India, the Indian Himalayan Region (IHR) is among the most landslide-prone areas, with intense monsoon rainfall frequently triggering slope failures that result in widespread damage and loss of life.

Addressing this growing challenge, researchers at the Indian Institute of Technology (IIT) Mandi have developed a fully operational Landslide Early Warning System (LEWS) that provides daily landslide forecasts across the Himalayan region.

The research has been led by Professor Dericks Praise Shukla from the School of Civil and Environmental Engineering, IIT Mandi, along with research scholars Ankit Singh and Nitesh Dhiman.

WHAT IS THE LANDSLIDE EARLY WARNING SYSTEM?

A Landslide Early Warning System (LEWS) is designed to forecast and monitor the likelihood of landslides by combining information on terrain susceptibility with real-time rainfall data. The system identifies areas at elevated risk and issues timely warnings, enabling authorities and disaster management agencies to take preventive measures such as evacuation and emergency preparedness.

Highlighting the importance of the initiative, Professor Dericks Praise Shukla said, "At the very onset of the monsoon, our Landslide Early Warning System (LEWS) provides daily landslide forecasts through a web-based application."

"The system is designed to help identify high-risk areas in advance, enabling authorities and communities to undertake timely evacuation and disaster preparedness measures," he adds.

He added that satellite-based early warning systems are among the most effective investments in disaster risk reduction because they convert scientific data into timely, actionable information.

A region-wide forecasting platform such as LEWS can significantly improve preparedness, accelerate emergency response, and strengthen coordination among disaster management agencies, particularly during the monsoon season when landslide risks are at their peak.

COVERS THE ENTIRE INDIAN HIMALAYAN REGION

Unlike several existing landslide early warning systems in India that are limited to specific locations, IIT Mandi's LEWS has been developed for the entire Indian Himalayan Region, making it one of the country's most extensive landslide forecasting systems.

The research team adopted a multi-stage methodology to build the forecasting platform.

The first stage involved identifying nearly 26,000 historical landslides from the Geological Survey of India (GSI) database to prepare a comprehensive landslide susceptibility map. Multiple terrain and environmental factors responsible for triggering landslides were analysed using ensemble machine learning models.

The researchers then developed the Probability of Rainfall-Induced Landslides (P-RIL) model using data from NASA's Global Landslide Catalogue along with seven rainfall parameters obtained from IMERG satellite datasets. Since rainfall patterns constantly change, the model dynamically incorporates rainfall information from the previous 15 days to generate updated risk assessments.

The final daily landslide forecast is produced by integrating the static susceptibility map with the dynamic P-RIL model through a probabilistic approach. The forecasts are presented using percentile-based risk categories, making them easier for users to interpret.

WEB PORTAL WITH FORECASTS, BULLETINS AND WHATSAPP ALERTS

To ensure easy access for government agencies and other stakeholders, the IIT Mandi team has developed a Google Earth Engine (GEE)-based web portal.

The platform allows users to:

• View landslide forecasts for the current day and the previous three days.
• Download daily forecast bulletins in PDF format.
• Receive location-specific landslide alerts through WhatsApp.

According to the researchers, the operational Landslide Early Warning System is expected to significantly enhance disaster preparedness and risk reduction efforts across the Indian Himalayan Region by providing timely, location-specific warnings that can help reduce both human and economic losses.