18 AI-Driven and Machine Learning Approaches in Geospatial Modeling for Hydrologic and Geologic Mapping

This session features GIS, remote sensing, and AI-driven/machine learning approaches to geologic and hydrologic mapping, emphasizing predictive modeling, cross-scale analysis, and innovative workflows that extend beyond traditional cartographic methods.

Tuesday, 10 March 2026
8:30 AM - 9:30 AM
RCC, 104

Participants

• Chair Youngsang Kwon
• Chair Reza Soltanian
• Chair Farzad Moeini

Presentations

• 8:30 AM
  Machine Learning for Supporting Digital Mapping of Soil Profile to Monitor Copper Distribution in the Brazilian Cerrado
  Ronny Barbosa, Federal University of Piauí, Teresina, PI and Paulo Dalto, Federal University of Piauí, Bom Jesus, PI and Vanessa Martins, Federal University of Piauí, Teresina, PI and Ozeas Costa Jr, The Ohio State University, Mansfield, OH
  411512 (Technical Sessions)
• 8:45 AM
  Modeling Urban Expansion and Heat Risk in Northwest Arkansas Using Remote Sensing and Machine Learning: A Multi-Scale Assessment of Land Use Change and Surface Temperature Dynamics
  Rasool Vahid Belarghou, University of Arkansas, Fayetteville, Arkansas and Mohamed Aly, University of Arkansas, Fayetteville, Arkansas
  411579 (Technical Sessions)
• 9:15 AM
  “A Cool- and Warm-Season Streamflow Reconstruction for Memphis, Tennessee”
  Dorian Burnette, University of Memphis, Memphis, Tennessee and Ray Lombardi, University of Memphis, Memphis, Tennessee and Youngsang Kwon, University of Memphis , Memphis, Tennessee
  411690 (Technical Sessions)