Mandi: There has been a considerable rise in landslides all
around the world as a result of climate change. The Indian Himalayan Region
(IHR) is highly susceptible to such disasters due to this, which results in
numerous slope failures and causes heavy casualties in terms of both lives and
properties. To tackle this problem, scientists from the Indian Institute of
Technology (IIT) Mandi have developed a fully functional LEWS system.
The
research has been led by Prof. Dericks Praise Shukla from the School of
Civil and Environmental Engineering, IIT Mandi, along with his research
scholars Mr. Ankit Singh and Mr. Nitesh Dhiman.
A
Landslide Early Warning System (LEWS) is a warning system which forecasts and
monitors the probability of landslides based on data regarding the
susceptibility of the topography along with rainfall in real time. LEWS issues
warnings to the regions where landslide risks exists so that necessary
precautions can be taken by the concerned authorities and disaster management
bodies.
Highlighting
the significance of the system, 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
further said that satellite-based early warning systems are among the most
effective investments in disaster risk reduction as they transform scientific
data into timely, actionable decisions. 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 at
their highest.
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
system has been created by the research group through a multi-stage approach.
At first, almost 2,6000 landslides were identified from the Geological
Survey of India (GSI) database to create a map of landslide susceptibility.
A variety of landslide triggering factors were combined using ensemble machine
learning models.
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 a dynamic one
because it makes use of rainfall data from the past 15 days.
The
final daily landslide prediction was calculated through the integration of the
static susceptibility map and the dynamic P-RIL model based on probability
analysis. For better interpretation of the predictions, percentile-based
categories of risks are used.
The
daily landslide forecast is derived using the probabilistic approach of
combining the static susceptibility map with the dynamic P-RIL model. For
making the outputs understandable for the users, the landslide forecasts are
provided in terms of risk categories using percentiles.
To
facilitate easy access and dissemination of information to the stakeholders,
the IIT Mandi team has developed a Google Earth Engine (GEE) based web
portal through which users can view landslide forecasts for the current day
along with the previous three days. Furthermore, users can also download
bulletins in PDF format and get WhatsApp alerts of the chosen locations.
According
to the researchers, the operation of the Landslide Early Warning System will
immensely help disaster preparedness and risk reduction initiatives within the
region by giving out timely and location specific warnings to reduce economic
damages.
Landslide
Early Warning System can be viewed at: