Shimla/Mandi: Researchers at IIT Mandi have developed an early warning system for landslides in the Himalayan region to boost disaster preparedness and risk reduction, the institute said on Wednesday.
The Indian Himalayan Region (IHR) is highly susceptible to landslides, which result in numerous slope failures, causing loss of life, as well as property. The newly developed Landslide Early Warning System (LEWS) forecasts and monitors the probability of landslides using data on topographic susceptibility and real-time rainfall, it said in a statement.
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"In contrast to other landslide early warning systems in India, which have their limitations in terms of the geographic scale, the LEWS implemented by IIT Mandi is applied throughout the Indian Himalayan region and hence one of the most extensive systems designed for the country," the statement said.
LEWS issues warnings to the regions displaying landslide risk so that the authorities concerned and disaster management bodies can take necessary precautions, it said.
The research was led by Professor Dericks Praise Shukla from the School of Civil and Environmental Engineering, Indian Institute of Technology (IIT) Mandi, along with research scholars Ankit Singh and Nitesh Dhiman.
"At the very onset of the monsoon, our 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," Professor Shukla said.
Satellite-based early warning systems are among the most effective investments in disaster risk reduction; they transform scientific data into timely, actionable decisions, he said.
"A region-wide landslide forecasting platform like this has the potential to significantly strengthen preparedness, enable faster response, and enhance coordination among disaster management agencies, particularly during the monsoon season when landslide risks are highest," Shukla added.
The research group created the system through a multi-stage approach. At first, almost 26,000 landslides were identified from the Geological Survey of India (GSI) database to create a map of landslide susceptibility. It was combined with the data on landslide-triggering factors using ensemble machine learning models, the institute said.
"Following this, the P-RIL (Probability of Rainfall-Induced Landslides) model was constructed using information derived from the NASA Global Landslide Catalogue and seven rainfall parameters collected from IMERG satellite datasets. Since rainfall conditions are always changing, the P-RIL model is dynamic because it uses rainfall data from the past 15 days," the statement said.
The final daily landslide prediction is calculated by integrating the static susceptibility map and the dynamic P-RIL model based on probability analysis. Percentile-based categories of risks are used for better interpretation of the predictions, according to the institute.
To make the outputs understandable for users, landslide forecasts are provided in terms of risk categories using percentiles.
The IIT Mandi team has developed a Google Earth Engine-based web portal through which users can view landslide forecasts for the current day, along with the previous three days. The institute said this move aims to facilitate easy access and dissemination of information to the stakeholders.
Furthermore, users can download bulletins in PDF format and receive WhatsApp alerts for chosen locations, it added.
According to the researchers, the operation of the LEWS will immensely help disaster preparedness and risk reduction initiatives within the region by issuing timely and location-specific warnings to reduce economic damages.
