The unpredictability of natural disasters makes them all
the more terrifying, but researchers at IIT Mandi claim to
have engineered a solution using Artificial Intelligence (AI)
and Machine Learning (ML). A new algorithm has been developed
by the team that could improve the accuracy of prediction for
natural hazards like landslides.
The unpredictability of natural disasters makes them all the more
terrifying, but researchers at IIT Mandi claim to have engineered a
solution using Artificial Intelligence (AI) and Machine Learning (ML).
A new algorithm has been developed by the team that could improve
the accuracy of prediction for natural hazards like landslides.
According to a report by PTI, the results of their work have recently
been published in the journal Landslides. The new algorithm has been
tested for landslides and the institute officials claim that it can
be applied to other natural phenomena as well, such as floods,
avalanches, extreme weather events, rock glaciers and
permafrost — mappings which tend to have very less data points,
helping to estimate the risks.
Speaking of landslide prediction, the officials mention that the
algorithm developed can tackle the challenge of data imbalance for
Landslide Susceptibility Mapping (LSM) in a given area. "To estimate
and eventually mitigate these risks, it is essential to identify
areas that are susceptible to landslides," said DP Shukla, Associate
Professor, School of Civil and Environmental Engineering, IIT Mandi.
"An LSM indicates the likelihood of a landslide occurring in a
specific area based on causative factors such as slope, elevation,
geology, soil type, distance from faults, rivers and faults, and
historical landslide data. The use of AI is becoming increasingly
vital for the prediction of natural disasters such as landslides.
They can potentially predict extreme events, create hazard maps,
detect events in real-time, provide situational awareness, and
support decision-making," he explained.
Professor Shukla added that Machine Learning was a subfield of AI
that enabled computers to learn and improve from experience, without
being explicitly programmed. "It is based on algorithms that can
analyse data, identify patterns, and make predictions or decisions,
much like human intelligence. ML algorithms, however, require
large amounts of training data for accurate prediction. In the
case of LSM, this data consists of the causative factors of
landslides as mentioned earlier, and historical landslide data," he said.
He further explained that landslides were a rare occurrence in
certain areas, leading to the unavailability of extensive amounts
of training data, which hindered the performance of ML algorithms.
"For a given area, in comparison to non-landslide points
(considered as negative), landslide points (considered as positive)
are very less thus creating an imbalance between positive and negative
points which affects the prediction," he said, as per PTI.
The new algorithm developed by his team overcomes this issue of
data imbalance for training, the professor claims. "It uses two
under-sampling techniques, EasyEnsemble and BalanceCascade, to
address the issue of imbalanced data in landslide mapping. Data
about the landslides that occurred in the Mandakini River Basin in
northwest Himalaya, Uttarakhand, India, between 2004 and 2017 were
used to train and validate the model," he said.
Speaking more about the research, the professor stated, "The results
showed that the algorithm significantly improved the accuracy of the
LSM, particularly when compared to traditional Machine Learning
techniques such as Support Vector Machine and Artificial Neural Network.
This new ML algorithm highlights the importance of data balancing in
ML models and demonstrates the potential for new technologies to drive
significant advancements in the field."
"It also underscores the critical need for large amounts of data to
accurately train ML models, particularly in the case of geohazards
and natural disasters where the stakes are high and human safety is
at risk," Shukla added. The researchers believe that the study
opens up new avenues in the field of LSM and other geohazard mapping
and management, helping to minimise the risks posed to human safety
and property, as per PTI.
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System