Research partners unite to develop predictive analytics platform for extreme cold weather operations
In the same way a helmsman guides a ship through turbulent waters, a team of researchers led by the University of North Dakota (UND) is developing an AI-powered system to navigate the growing complexities of the Arctic.
At the core of this initiative is a powerful Kubernetes (K8s) cluster — named after the Ancient Greek term for “helmsman” — which will drive the Arctic Knowledge-Based System (A-KBS), an advanced computational platform designed to support decision-making in cold weather environments using real-time data analytics and long-term forecasting. (Editor’s note: As Google explains, a Kubernetes cluster is a single, unified system composed of multiple computers that cooperate to execute applications.)
Why it matters
The Arctic is a challenging environment for operations. Advancements in AI and machine learning are enabling the analysis of vast amounts of satellite, lidar, geological, and geospatial data to enhance situational awareness leading to more efficient and resilient operations. Supported by a research contract from the US Army Corps of Engineers Research and Development Center, the A-KBS marks the first deployment of a Kubernetes cluster with supercomputing capabilities at UND.
“Challenges in the Arctic require supercomputing-level support for advanced situational awareness, real-time monitoring, and predictive analytics,” said Principal Investigator Timothy Pasch, professor of Communication (UND College of Arts and Sciences), associate director of the UND AI Research (AIR) Center and director of the UND ARCTIC Lab.
“As the Arctic increasingly becomes a global focal region, leveraging AI-driven modeling, Earth-scale data science, and remote sensing technology is essential for maintaining stability, sovereignty, and resilience.”
Co-leading the project is Naima Kaabouch, Chester Fritz Distinguished Professor of Computer Science and director of the UND AI Research (AIR) Center in the College of Engineering and Mines, with Aaron Bergstrom, senior computational scientist at UND’s Computational Research Center (Office of the Vice President for Research and Economic Development) managing the University’s High Performance Computing Cluster.
A computing powerhouse — accessible from anywhere
The A-KBS will handle an unprecedented amount of data including satellite imagery, LiDAR scans of Arctic infrastructure, and global-scale geospatial datasets as inputs into AI-driven decision support tools. For some of the most complex datasets, a Science Gateway developed in collaboration with the National Center for Ecological Analysis and Synthesis (NCEAS) led by Matt Jones together with Ian Nesbitt, will deploy the Globus Compute system to securely transmit data to the San Diego Supercomputing Center (under the supervision of Rick Wagner, chief technology officer) for large-scale processing with the EXPANSE Supercomputer, where Pasch has been allocated NSF ACCESS computational credits. https://www.sdsc.edu/systems/expanse/
“This advanced cyberinfrastructure enables geospatial forecasting extending 5, 10, or even 20 years — critical for Arctic infrastructure investment and national preparedness,” said Bergstrom. “These predictive analytics reduce costs and improve resilience in an extreme cold weather environment changing at extraordinary speed.”
A national collaboration for Arctic resilience
The UND-led initiative brings together researchers from across the country. A $100,000 subaward supports collaborative research with SUNY Stony Brook, where Dr. Dilip Gersappe, chair of Materials Science and Chemical Engineering, is developing new sustainable freeze-resisting materials, called hydrogels, aiming to mitigate extreme cold weather damage to infrastructure and helping to reduce the billion-dollar costs of Arctic degradation and repair.
Sherif Abdelaziz, professor of Engineering at Virginia Tech, is also a major collaborator on the A-KBS project, focusing on engineering resilient extreme cold weather infrastructure, including extreme cold temperature effects on soils. He will work with students and faculty partners leveraging UND computation to help analyze permafrost stability, soil-structure interactions and long-term Arctic infrastructure resilience.
UND team and research integration
At UND, graduate students Andrew Wilcox (MS, Earth Systems Science) Mya Geisinger and Sheridan Parker (MA/PhD, Communication), Mary Soaper and Hayden Patterson (BS/MS Geology), and Research Engineer II Aymane Ahajjam (PhD, Computer Science), alongside faculty Xudong Yu (Comm), DjeDje Kossu Zahui (CEM) and Marcus Algaier (Physics) collaborate closely with Pasch. Computer Science PhD students Saisri and Srinilla Pogalla, Software Engineers Stephen Miller and Walker McKee, CI Engineer Brad Traver, and System Administrator Robert Peterson collaborate on A-KBS computational development with Aaron Bergstrom.
Computer Science graduate students William Valentine, Meissam Shayeghmoradi, Kyle Foerster, David Jumar Bacallo, David Pappe, Howard Hottinger, and Nafiul Nawjis work closely with Kaabouch. Partners from UND’s Research Institute for Autonomous Systems (RIAS) including Scott Keane, Emmanuel Chukwuemeka, Zach Reeder and others are contributing to data collection efforts in Alaska, leveraging a combination of aerial and terrestrial sensors. Project Manager Kyle Buzek ensures integration and optimization of all research.
Pasch emphasized the importance of collaboration, citing UND’s Grand Challenges of Computational and Data Science and National Security and Space: “We are incredibly enthusiastic about this research,” he said. “The scope of the Kubernetes Cluster we are constructing has the speed and capability to provide real-time situational awareness and decision support — allowing us to predict and visualize Arctic geospatial change with unparalleled accuracy in one of the world’s most rapidly evolving frontiers.”
