Dr. Paheding from the School of Engineering and Computing leads a $400K NSF Effort on Deep Learning for Extreme Weather Events

Summary: The impacts of climate change have led to an increase in extreme weather events, posing significant challenges to infrastructure resilience and community well-being. Research supported by this NSF Disaster Resilience Research Grant (DRRG) addresses the critical need to monitor and maintain existing infrastructure in the face of these challenges. Specifically, it focuses on mine tailings impoundments, massive geotechnical structures that store mining waste. The failure of these structures during extreme weather events can cause environmental damage and loss of life. By leveraging satellite imagery analysis, weather data, and deep learning techniques, this project aims to establish a standard monitoring approach for mine tailings impoundments and revolutionize infrastructure monitoring and hazard management. The outcomes will enable the identification of movements within these structures and provide a predictive understanding of failure probability, allowing us to act proactively and prevent disasters. This monitoring approach will enhance community resilience, support hazard management, and establish critical risk profiles for surrounding areas.