Integrated Porosity and Pollutant Tracking in Engineering Geology: A Multi-Class Classification Supervised Machine Learning Approach – A Case Study in Ilorin, Kwara State, Nigeria

The world today faces unprecedented environmental challenges, including climate change, clean water scarcity, ocean contamination, and groundwater pollution, largely due to inadequate technology for tracking environmental pollutants. This study introduces a Supervised Machine Learning (SML) framework using multi-class classification to assess porosity and pollutant tracking in Ilorin, Kwara State, Nigeria. The study area is located within latitudes 8°44’6†N and 7°59’40†N and longitudes 4°09’40†E and 5°14’8†E, all within Nigeria’s basement complex. This research formulates a robust SML model for multi-class classification, categorizing different environmental suitability levels based on porosity, pollutant tracking, and environmental factors. The case study in Ilorin demonstrates the model’s effectiveness, contributing significantly to the field of engineering geology. A comprehensive approach integrates geological, geotechnical, geophysical, and environmental datasets. Surface and subsurface investigations, combined with supervised SML methods, predict suitability for porosity and pollutant tracking, providing insights into complex relationships impractical for manual analysis.