Data-Driven Framework for Predicting Subsurface Contamination Pathways in Complex Remediation Projects

• Author(s): Omolola Badmus; Azeez Lamidi Olamide
• Paper ID: 1713077
• Page: 312-335
• Published Date: 30-11-2018
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 2 Issue 5 November-2018

Abstract

Effective remediation of contaminated sites increasingly depends on advanced predictive capabilities that can accurately characterize and forecast subsurface contamination pathways. Traditional site assessment methods often struggle to capture the spatial heterogeneity, nonlinear contaminant transport dynamics, and multi-source pollution interactions typical of complex remediation projects. This study proposes a comprehensive data-driven framework that integrates geospatial analytics, machine learning models, and hydrogeological simulation to enhance the prediction of contaminant migration in heterogeneous subsurface environments. The framework leverages high-resolution datasets including soil properties, hydrological gradients, geochemical indicators, and historical contaminant concentrations to identify key transport mechanisms and generate predictive contamination plume trajectories. By combining supervised learning algorithms with physics-informed constraints, the model captures both the statistical patterns and mechanistic behaviors governing subsurface pollutant movement. In addition, the framework incorporates uncertainty quantification techniques to evaluate prediction confidence and guide decision-making under data limitations. Case applications demonstrate that the data-driven approach outperforms traditional deterministic models in forecasting plume evolution, delineating risk zones, and identifying potential receptor exposure pathways. Results further show that integrating multi-source datasets significantly improves model robustness, offering actionable insights for remediation design, resource allocation, and long-term monitoring strategies. The study contributes a scalable methodology capable of supporting remediation engineers, environmental regulators, and policymakers in optimizing site-specific and regional contamination management. By bridging advanced analytics with domain knowledge, the proposed framework supports early detection of contamination hotspots, enhances risk assessment, and promotes cost-effective remediation planning. Ultimately, this data-driven predictive architecture represents a transformative tool for managing subsurface contamination under increasing environmental and regulatory pressures, enabling more precise, transparent, and adaptive remediation interventions. Future work will explore real-time data integration, improved interpretability of machine learning models, and incorporation of emerging sensing technologies to further strengthen predictive accuracy and support sustainable environmental restoration.

Keywords

Subsurface Contamination, Data-Driven Modeling, Machine Learning, Hydrogeology, Remediation Projects, Contaminant Transport, Predictive Analytics, Environmental Monitoring, Uncertainty Quantification, Geospatial Analysis.

Citations

IRE Journals:
Omolola Badmus, Azeez Lamidi Olamide "Data-Driven Framework for Predicting Subsurface Contamination Pathways in Complex Remediation Projects" Iconic Research And Engineering Journals Volume 2 Issue 5 2018 Page 312-335

IEEE:
Omolola Badmus, Azeez Lamidi Olamide "Data-Driven Framework for Predicting Subsurface Contamination Pathways in Complex Remediation Projects" Iconic Research And Engineering Journals, 2(5)