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IIT Guwahati's AI Model Predicts 492 New Glacial Lakes in Eastern Himalayas

Researchers from IIT Guwahati have developed a predictive framework using AI and satellite imagery to forecast where new glacial lakes may form in the Eastern Himalayas. The model identified 492 high-risk locations, providing crucial data for managing Glacial Lake Outburst Flood (GLOF) hazards. The Bayesian Neural Network (BNN) method proved most accurate, using landscape features like gentle slopes and retreating glaciers as key predictors. This adaptable tool supports climate-resilient infrastructure planning and global disaster-risk reduction.

Key Points: IIT Guwahati AI Predicts Himalayan Glacial Lake Hazards

  • Predictive AI framework developed
  • 492 potential glacial lake sites identified
  • Aims to guide early-warning for floods
  • Tool adaptable to global mountain regions
2 min read

IIT Guwahati develops method to monitor glacial hazards in Eastern Himalayas

IIT Guwahati researchers use AI to identify 492 potential new glacial lake sites in the Eastern Himalayas, aiding GLOF risk management and water planning.

"It offers a practical tool for reducing risks to communities and infrastructure in the Himalayas. - Prof. Ajay Dashora"

Guwahati, Jan 27

Researchers from the Indian Institute of Technology Guwahati have developed a predictive framework that has identified 492 locations where glacial lakes are likely to form in the Eastern Himalayan mountains.

The research conducted using high-resolution Google Earth images and digital elevation models also provides crucial insights for hazard management and water-resource planning in high-mountain regions.

The models helped capture complex landscape features and estimate uncertainty in the predictions, making the forecasts more realistic and reliable.

With the developed framework, the research team identified 492 locations in the Eastern Himalaya where new glacial lakes are likely to form, thereby indicating areas that require careful monitoring and preventive measures.

"By pinpointing high-risk areas, the framework can guide early-warning systems for Glacial Lake Outburst Floods (GLOFs), help plan safer locations for roads, hydropower projects, and settlements, and support long-term water-resource management. It offers a practical tool for reducing risks to communities and infrastructure in the Himalayas," said Prof. Ajay Dashora, Assistant Professor, Department of Civil Engineering, IIT Guwahati.

"Beyond hazard management, the method can help understand how water systems may change as glaciers continue to retreat. Importantly, the framework is adaptable to other glaciated mountain regions around the world, making it a valuable tool for climate-resilient planning and disaster-risk reduction globally," Dashora added.

The findings, published in Nature's Scientific Reports journal, confirm that the shape and structure of the land, often overlooked in previous studies, can play a central role in where and how a glacial lake may appear.

In the development process, the research team tested three predictive methods, including Logistic Regression (LR), Artificial Neural Network (ANN), and Bayesian Neural Network (BNN)

Among these, the research team found the Bayesian Neural Network (BNN) to be the most accurate and showed that certain earth features, such as neighbouring lakes, cirques, gentle slopes, and retreating glaciers, are the strongest predictors of glacial lake formation.

The team now plans to integrate moraine development histories, automate data preparation, and add field-based validation to the developed framework.

These improvements will enhance the model's accuracy and broaden its use for large-scale monitoring of glacial hazards.

- IANS

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Reader Comments

P
Priya S
As someone from Assam, this research feels very close to home. We've seen the impacts of floods. Using AI/ML models like BNN for such critical predictions is the way forward. Hope the government actively uses this framework for infrastructure planning in the hills.
R
Rohit P
Great initiative, but the real test is implementation. We have brilliant research, but often the gap between the lab and the field is huge. Hope the NDMA and state disaster management authorities collaborate with IIT-G on this. Action is needed, not just reports.
S
Sarah B
This is a globally significant study. The fact that it's adaptable to other mountain regions is key. Climate change is a shared challenge. Kudos to the team for publishing in Scientific Reports.
V
Vikram M
492 potential lakes! That's a staggering number. It shows the scale of the change happening in our mountains. This tech can help plan hydropower projects more safely, which is vital for our energy needs. Jai Hind!
K
Karthik V
Respectfully, while the research is top-notch, I hope the "field-based validation" happens soon and involves local communities. Their traditional knowledge of the landscape is invaluable and should be integrated with this high-tech model for the best results.
A
Ananya R

We welcome thoughtful discussions from our readers. Please keep comments respectful and on-topic.

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