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. The study area includes Sokoto1, Sokoto2, Malete, Oke Oyi, Jimba, Omu Aran, and Ijagbo, all within Kwara State, which experiences cyclical dry and rainy seasons. Environmental factors considered include geological, geotechnical, geophysical, land use, water surface, and slope aspects. The predictive model, utilizing multi-factorial analysis, categorizes outcomes into Highly Suitable, Moderately Suitable, and Not Suitable. Key factors influencing porosity and pollutant tracking include Suitability, Environmental Factors, Sub-Factors, Rating, Percentage of Influence, and Class. Model performance evaluation includes a fit analysis and confusion matrix. The predictive model, trained on diverse environmental datasets, effectively categorizes suitability levels for porosity and pollutant tracking. The study identifies candidate sites with higher porosity and lower permeability, demonstrating practical applicability in decision-making processes for environmental analysis and engineering geology related to surface and underground pollutant tracking.