IIT Mandi develops AI algorithm to improve accuracy of landslide prediction

The developed algorithm has been tested for landslides and can be applied to other natural phenomena such as floods, avalanches, extreme weather events, rock glaciers and permafrost, mapping that tend to have very less data points, helping to estimate the risks. 

Indian Institute of Technology (IIT) Mandi researchers have developed a new algorithm using artificial intelligence and machine learning that could improve the accuracy of prediction for natural hazards.  

The algorithm developed by Dericks Praise Shukla, associate professor, School of Civil and Environmental Engineering, IIT Mandi, and Sharad Kumar Gupta, former research scholar, IIT Mandi, currently working at Tel Aviv University (Israel), can tackle the challenge of data imbalance for landslide susceptibility mapping which represents the likelihood of landslide occurrences in a given area. 

A landslide susceptibility mapping 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. 

Shukla’s team has developed a new ML algorithm that overcomes this issue of data imbalance for training of the algorithm. 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. 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. 

Shukla said, “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.” 

He believes that this study opens up new avenues in the field of LSM and other geohazard mapping and management. It can be applied to other phenomena such as floods, avalanches, extreme weather events, rock glaciers and permafrost, helping to minimise the risks posed to human safety and property.  

Read more: 

• Courts orders forensic probe into death of IITKGP student
• GATE 2023 Answer Key Out today at gate.iitk.ac.in
• IITGN extends application deadline for summer research internship program