08/17/2026
🚀 Get ready for the AEG 2026 AI/Machine Learning Virtual Symposium! Meet one of our six incredible speakers, Dr. Thomas Oommen, Professor and Chair, Department of Geology and Geological Engineering, University of Mississippi
Abstract:
Digging Deeper with Data: Artificial Intelligence as a Force Multiplier in Environmental and Engineering Geology Artificial intelligence is reshaping environmental and engineering geology by enabling more accurate, scalable, and timely analysis of subsurface conditions, geological hazards, and earth-surface processes critical to infrastructure planning and risk management. Machine learning and deep learning approaches now support landslide susceptibility mapping, ground deformation monitoring, and soil characterization at spatial scales and resolutions previously unattainable through conventional field-based or numerical methods. In environmental geology, AI-driven remote sensing platforms such as Google Earth Engine facilitate continuous monitoring of surface water quality, soil moisture dynamics, contamination indicators, and land-cover change, providing actionable intelligence for groundwater protection, remediation planning, and regulatory compliance. Physics-informed neural networks and hybrid models that integrate geomechanical and hydrogeological domain knowledge into data-driven frameworks offer physically consistent predictions suited to the interpretability demands of engineering practice. However, translating AI research into operational geotechnical and environmental workflows presents persistent challenges, including limited labeled training data in geologically complex settings, model transferability across site conditions, and the need for uncertainty quantification in safety-critical applications. Advancing practical AI for environmental and engineering geology requires standardized benchmark datasets, reproducible end-to-end pipelines, and governance frameworks ensuring that model outputs meet the accountability and reliability standards expected in professional geoscience practice. adaptation of the base model to new geomorphic settings with as few as 600–700 additional training landslides, yielding substantial performance gains. We demonstrate results across diverse landslide morphologies, including small translational failures in Kentucky, long-runout debris flows in North Carolina, and low-angle glaciolacustrine landslides in the Western Canadian Sedimentary Basin, highlighting the model's potential for broad application.
Register now at https://www.aegevents.org/ai-machine-learning-virtual-symposium
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