Researchers at the Indian Institute of Technology (IIT) Mandi have developed a fully operational Landslide Early Warning System (LEWS) designed to provide daily landslide forecasts for the entire Indian Himalayan Region (IHR). The system integrates real-time rainfall data with terrain susceptibility to enable authorities to take timely preventive measures during the monsoon season.
System Overview and Significance
The Landslide Early Warning System predicts the probability of landslides by combining topographic susceptibility with real-time rainfall information. It continuously monitors changing weather conditions and issues location-specific warnings, allowing disaster management authorities and local administrations to implement evacuation plans and other preparedness measures in advance. The Indian Himalayan Region is among the most landslide-prone areas in the country, with climate change contributing to an increasing frequency of extreme rainfall events and slope failures, resulting in significant loss of life, damage to infrastructure, and economic losses every year.
Research Leadership and Methodology
The research was led by Prof Dericks Praise Shukla from the School of Civil and Environmental Engineering, IIT Mandi, along with research scholars Ankit Singh and Nitesh Dhiman. The team adopted a multi-stage scientific approach to develop the platform. Initially, nearly 26,000 landslide records from the Geological Survey of India (GSI) database were analysed to prepare a comprehensive landslide susceptibility map. Various terrain and environmental factors influencing landslides were integrated using advanced ensemble machine learning techniques.
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 conditions change continuously, the dynamic P-RIL model incorporates rainfall information from the previous 15 days to estimate the likelihood of landslide occurrence.
Forecast Generation and Accessibility
The final daily landslide forecast is generated by combining the static susceptibility map with the dynamic P-RIL model using probabilistic analysis. To make the forecasts easier to interpret, the results are presented as percentile-based risk categories, enabling users to quickly identify areas facing low to very high landslide risk. Unlike many existing landslide warning systems in India that are limited to specific regions, IIT Mandi's LEWS has been developed for the entire Indian Himalayan Region, making it one of the country's most extensive operational landslide forecasting systems.
To ensure wider accessibility, the IIT Mandi team has also developed a Google Earth Engine (GEE)-based web portal that allows users to view landslide forecasts for the current day as well as the previous three days. The platform also enables users to download forecast bulletins in PDF format and receive WhatsApp alerts for selected locations.
Expert Remarks
Highlighting the significance of the innovation, Prof 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 added that satellite-based early warning systems represent one of the most effective investments in disaster risk reduction because they transform scientific data into timely, actionable decisions. According to him, a region-wide landslide forecasting platform has the potential to significantly strengthen preparedness, improve emergency response and enhance coordination among disaster management agencies during the monsoon season, when landslide risks are at their peak.
