Thomas Oommen, Ph.D.

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.​

Bio:

Thomas Oommen began his academic career at Michigan Technological University, serving 13 years in geological engineering and progressing from assistant to associate to professor. He has contributed significantly to understanding earth materials, geologic processes, and geohazards, applying those insights to engineering and hazard mitigation. Oommen’s research leverages remote sensing and machine learning to address critical issues in site characterization, infrastructure monitoring, and geohazards. Read more ...

Paul Cleverley

Professor of Information Science & Technology at Robert Gordon University in Scotland

Abstract:

AI Ethics: Using Large Language Models in the Geosciences

​​​​​

Large Language Models (LLMs) should be used responsibly in the geosciences, weighing their benefits against significant risks including over-trust, bias, reduced reproducibility, data privacy concerns, sycophancy, and environmental cost. The presentation distinguishes critical thinking from ethics, and argues that the decision to use AI is context-dependent. Sometimes the most ethical choice is not to use it at all. Drawing on a conceptual model of AI digital literacy grounded in self-reflexivity and human oversight, it sets out what "good" looks like for geoscientists working with AI. This includes using it to support rather than replace professional judgment, maintaining transparency and traceability, addressing bias and fairness, and exercising epistemic humility. The central message is that LLMs do not reduce risk so much as redistribute it, and that uncritical use is itself a core ethical hazard.

Bio:

Paul is a geologist and computer scientist by background. He currently serves as Visiting Professor of Information Science & Technology at Robert Gordon University in Scotland, and is Chair of the AI Ethics Task Group of the Geoethics Commission of the International Union of Geological  Sciences (IUGS)