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.
Sources:
Edex Live
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System
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