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Data-Driven Framework for Predicting Subsurface Contamination Pathways in Complex Remediation Projects
Subject area: Science,Engineering and Technology · Area of research: Data-Driven Framework
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.
References
[1] Ahmed, F. (2017, October). An IoT-big data based machine learning technique for forecasting water requirement in irrigation field. In International conference on research and practical issues of enterprise information systems (pp. 67-77). Cham: Springer International Publishing.
[2] Akpan, U. U., Adekoya, K. O., Awe, E. T., Garba, N., Oguncoker, G. D., & Ojo, S. G. (2017). Mini-STRs screening of 12 relatives of Hausa origin in northern Nigeria. Nigerian Journal of Basic and Applied Sciences, 25(1), 48-57.
[3] Alibakhshi, S., Groen, T. A., Rautiainen, M., & Naimi, B. (2017). Remotely-sensed early warning signals of a critical transition in a wetland ecosystem. Remote Sensing, 9(4), 352.
[4] An, C. J., McBean, E., Huang, G. H., Yao, Y., Zhang, P., Chen, X. J., & Li, Y. P. (2016). Multi-soil-layering systems for wastewater treatment in small and remote communities. J. Environ. Inform, 27(2), 131-144.
[5] Andres, L., Boateng, K., Borja-Vega, C., & Thomas, E. (2018). A review of in-situ and remote sensing technologies to monitor water and sanitation interventions. Water, 10(6), 756.
[6] Ascui, F. (2014). A review of carbon accounting in the social and environmental accounting literature: what can it contribute to the debate?. Social and Environmental Accountability Journal, 34(1), 6-28.
[7] Ascui, F., & Lovell, H. (2012). Carbon accounting and the construction of competence. Journal of Cleaner Production, 36, 48-59.
[8] Awe, E. T. (2017). Hybridization of snout mouth deformed and normal mouth African catfish Clarias gariepinus. Animal Research International, 14(3), 2804-2808.
[9] Awe, E. T., & Akpan, U. U. (2017). Cytological study of Allium cepa and Allium sativum.
[10] Awe, E. T., Akpan, U. U., & Adekoya, K. O. (2017). Evaluation of two MiniSTR loci mutation events in five Father-Mother-Child trios of Yoruba origin. Nigerian Journal of Biotechnology, 33, 120-124.
[11] Barzegar, R., Moghaddam, A. A., Deo, R., Fijani, E., & Tziritis, E. (2018). Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms. Science of the total environment, 621, 697-712.
[12] Bello-Dambatta, A. (2010). The development of a web-based decision support system for the sustainable management of contaminated land. University of Exeter (United Kingdom).
[13] Bello-Dambatta, A., & Javadi, A. A. (2010). Contaminated land decision support: a review of concepts, methods and systems. Modelling of Pollutants in Complex Environmental Systems, 2, 43.
[14] Binley, A., Hubbard, S. S., Huisman, J. A., Revil, A., Robinson, D. A., Singha, K., & Slater, L. D. (2015). The emergence of hydrogeophysics for improved understanding of subsurface processes over multiple scales. Water resources research, 51(6), 3837-3866.
[15] Boriana, V. (2017). Urban Regeneration of Underused Industrial Sites in Albania (Doctoral dissertation).
[16] Bowen, F., & Wittneben, B. (2011). Carbon accounting: Negotiating accuracy, consistency and certainty across organisational fields. Accounting, Auditing & Accountability Journal, 24(8), 1022-1036.
[17] Buma, B., & Livneh, B. (2017). Key landscape and biotic indicators of watersheds sensitivity to forest disturbance identified using remote sensing and historical hydrography data. Environmental Research Letters, 12(7), 074028.
[18] Burritt, R. L., Schaltegger, S., & Zvezdov, D. (2011). Carbon management accounting: explaining practice in leading German companies. Australian accounting review, 21(1), 80-98.
[19] Cappuyns, V., & Kessen, B. (2014). Combining life cycle analysis, human health and financial risk assessment for the evaluation of contaminated site remediation. Journal of Environmental Planning and Management, 57(7), 1101-1121.
[20] Cheng, F., Geertman, S., Kuffer, M., & Zhan, Q. (2011). An integrative methodology to improve brownfield redevelopment planning in Chinese cities: A case study of Futian, Shenzhen. Computers, environment and urban systems, 35(5), 388-398.
[21] Derycke, V., Coftier, A., Zornig, C., Leprond, H., Scamps, M., & Gilbert, D. (2018). Environmental assessments on schools located on or near former industrial facilities: Feedback on attenuation factors for the prediction of indoor air quality. Science of the Total Environment, 626, 754-761.
[22] Deschaine, L. M. (2014). Decision support for complex planning challenges (Doctoral dissertation, Ph. D. Dissertation, Chalmers University of Technology, Göteborg, Sweden, 233p).
[23] Edwards, F. K., Baker, R., Dunbar, M., & Laizé, C. (2012). A review of the processes and effects of droughts and summer floods in rivers and threats due to climate change on current adaptive strategies.
[24] Essaid, H. I., Bekins, B. A., & Cozzarelli, I. M. (2015). Organic contaminant transport and fate in the subsurface: Evolution of knowledge and understanding. Water Resources Research, 51(7), 4861-4902.
[25] Faseemo, O., Massot, J., Essien, N., Healy, W., & Owah, E. (2009, August). Multidisciplinary Approach to Optimising Hydrocarbon Recovery From Conventional Offshore Nigeria: OML100 Case Study. In SPE Nigeria Annual International Conference and Exhibition (pp. SPE-128889). SPE.
[26] Felisa, G. (2015). Dynamics of coastal aquifers: data-driven forecasting and risk analysis.
[27] Ferdinand, A. V., & Yu, D. (2016). Sustainable urban redevelopment: Assessing the impact of third-party rating systems. Journal of Urban Planning and Development, 142(1), 05014033.
[28] Field, C. B. (Ed.). (2012). Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge University Press.
[29] Filippini, M. (2015). Geological and hydrogeological features affecting migration, multi-phase partitioning and degradation of chlorinated hydrocarbons through unconsolidated porous media.
[30] Francisca, F. M., Carro Perez, M. E., Glatstein, D. A., & Montoro, M. A. (2012). Contaminant transport and fluid flow in soils. Horizons in Earth Research. Nova Science Publishers, New York, 179-214.
[31] Furniss, M. J. (2011). Water, climate change, and forests: watershed stewardship for a changing climate. DIANE Publishing.
[32] Gibassier, D., & Schaltegger, S. (2015). Carbon management accounting and reporting in practice: a case study on converging emergent approaches. Sustainability Accounting, Management and Policy Journal, 6(3), 340-365.
[33] Gober, P., & Kirkwood, C. W. (2010). Vulnerability assessment of climate-induced water shortage in Phoenix. Proceedings of the National Academy of Sciences, 107(50), 21295-21299.
[34] Green, T. R. (2016). Linking climate change and groundwater. In Integrated groundwater management: Concepts, approaches and challenges (pp. 97-141). Cham: Springer International Publishing.
[35] Handmer, J., Honda, Y., Kundzewicz, Z. W., Arnell, N., Benito, G., Hatfield, J., ... & Yamano, H. (2012). Changes in impacts of climate extremes: human systems and ecosystems. Managing the risks of extreme events and disasters to advance climate change adaptation special report of the intergovernmental panel on climate change, 231-290.
[36] Hanson, R. T., Flint, L. E., Flint, A. L., Dettinger, M. D., Faunt, C. C., Cayan, D., & Schmid, W. (2012). A method for physically based model analysis of conjunctive use in response to potential climate changes. Water Resources Research, 48(6).
[37] Hardie, S. M. L., & McKinley, I. G. (2014). Fukushima remediation: status and overview of future plans. Journal of environmental radioactivity, 133, 75-85.
[38] Hartmann, F., Perego, P., & Young, A. (2013). Carbon accounting: Challenges for research in management control and performance measurement. Abacus, 49(4), 539-563.
[39] Herat, S., & Agamuthu, P. (2012). E-waste: a problem or an opportunity? Review of issues, challenges and solutions in Asian countries. Waste Management & Research, 30(11), 1113-1129.
[40] Hipsey, M. R., Hamilton, D. P., Hanson, P. C., Carey, C. C., Coletti, J. Z., Read, J. S., ... & Brookes, J. D. (2015). Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories. Water Resources Research, 51(9), 7023-7043.
[41] Hoek, G., Beelen, R., & Brunekreef, B. (2011). Methodological issues and statistical analysis in land use regression modeling. Epidemiology, 22(1), S101.
[42] Hou, D., & Al-Tabbaa, A. (2014). Sustainability: A new imperative in contaminated land remediation. Environmental Science & Policy, 39, 25-34.
[43] Hubbard, S. S., Williams, K. H., Agarwal, D., Banfield, J., Beller, H., Bouskill, N., ... & Varadharajan, C. (2018). The East River, Colorado, Watershed: A mountainous community testbed for improving predictive understanding of multiscale hydrological–biogeochemical dynamics. Vadose Zone Journal, 17(1), 1-25.
[44] Ike, P. N., Aifuwa, S. E., Nnabueze, S. B., Olatunde-Thorpe, J., Ogbuefi, E., Oshoba, T. O., & Akokodaripon, D. (2018). Utilizing Nanomaterials in Healthcare Supply Chain Management for Improved Drug Delivery Systems. medicine (Ding et al., 2020; Furtado et al., 2018), 12, 13.
[45] Jago-on, K. A. B., Kaneko, S., Fujikura, R., Fujiwara, A., Imai, T., Matsumoto, T., ... & Taniguchi, M. (2009). Urbanization and subsurface environmental issues: An attempt at DPSIR model application in Asian cities. Science of the total environment, 407(9), 3089-3104.
[46] Jayasooriya, V. M. (2016). Optimization of green infrastructure practices for industrial areas (Doctoral dissertation, Victoria University).
[47] Karandish, F., Darzi-Naftchali, A., & Asgari, A. (2017). Application of machine-learning models for diagnosing health hazard of nitrate toxicity in shallow aquifers. Paddy and Water environment, 15(1), 201-215.
[48] Karpatne, A., Ebert-Uphoff, I., Ravela, S., Babaie, H. A., & Kumar, V. (2018). Machine learning for the geosciences: Challenges and opportunities. IEEE Transactions on Knowledge and Data Engineering, 31(8), 1544-1554.
[49] Kato, S. (2010). Greenspace conservation planning framework for urban regions based on a forest bird-habitat relationship study and the resilience thinking. University of Massachusetts Amherst.
[50] Kobus, H., Barczewski, B., & Koschitzky, H. P. (Eds.). (2012). Groundwater and subsurface remediation: research strategies for in-situ technologies. Springer Science & Business Media.
[51] Koop, S. H., & van Leeuwen, C. J. (2017). The challenges of water, waste and climate change in cities. Environment, development and sustainability, 19(2), 385-418.
[52] Kresic, N., & Mikszewski, A. (2012). Hydrogeological conceptual site models: data analysis and visualization. CRC press.
[53] Kulawiak, M., & Lubniewski, Z. (2014). SafeCity A GIS-based tool profiled for supporting decision making in urban development and infrastructure protection. Technological Forecasting and Social Change, 89, 174-187.
[54] Kuppusamy, S., Palanisami, T., Megharaj, M., Venkateswarlu, K., & Naidu, R. (2016). In-situ remediation approaches for the management of contaminated sites: a comprehensive overview. Reviews of Environmental Contamination and Toxicology Volume 236, 1-115.
[55] Langat, P. K., Kumar, L., & Koech, R. (2017). Temporal variability and trends of rainfall and streamflow in Tana River Basin, Kenya. Sustainability, 9(11), 1963.
[56] Leeson, A., Stroo, H., Crane, C., Deeb, R., Kavanaugh, M., Lebron, C., ... & Simpkin, T. (2013). SERDP and ESTCP workshop on long term management of contaminated groundwater sites.
[57] Leibowitz, S. G., Comeleo, R. L., Wigington Jr, P. J., Weaver, C. P., Morefield, P. E., Sproles, E. A., & Ebersole, J. L. (2014). Hydrologic landscape classification evaluates streamflow vulnerability to climate change in Oregon, USA. Hydrology and Earth System Sciences, 18(9), 3367-3392.
[58] Lemming, G. (2010). Environmental assessment of contaminated site remediation in a life cycle perspective. Technical University of Denmark.
[59] Levy, L. C. (2013). Chasing fumes: The challenges posed by vapor intrusion. Nat. Resources & Env't, 28, 20.
[60] Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
[61] Liang, J. (2018). Development of Physically-Based and Data-Driven Models to Predict Contaminant Loads in Runoff Water From Agricultural Fields. University of California, Riverside.
[62] Maas, K., Schaltegger, S., & Crutzen, N. (2016). Integrating corporate sustainability assessment, management accounting, control, and reporting. Journal of cleaner production, 136, 237-248.
[63] Majone, M., Verdini, R., Aulenta, F., Rossetti, S., Tandoi, V., Kalogerakis, N., ... & Fava, F. (2015). In situ groundwater and sediment bioremediation: barriers and perspectives at European contaminated sites. New biotechnology, 32(1), 133-146.
[64] Mallants, D., Van Genuchten, M. T., Šimůnek, J., Jacques, D., & Seetharam, S. (2010). Leaching of contaminants to groundwater. In Dealing with Contaminated Sites: From Theory towards Practical Application (pp. 787-850). Dordrecht: Springer Netherlands.
[65] Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., ... & Toth, B. (2018). On the use of unmanned aerial systems for environmental monitoring. Remote sensing, 10(4), 641.
[66] Mark, B. G., Bury, J., McKenzie, J. M., French, A., & Baraer, M. (2010). Climate change and tropical Andean glacier recession: Evaluating hydrologic changes and livelihood vulnerability in the Cordillera Blanca, Peru. Annals of the Association of American geographers, 100(4), 794-805.
[67] McAlary, T. A., Provoost, J., & Dawson, H. E. (2010). Vapor intrusion. In Dealing with Contaminated Sites: From Theory towards Practical Application (pp. 409-453). Dordrecht: Springer Netherlands.
[68] McGrath, R., Reid, R., & Tran, P. (2017, January). EPRI Report: Review of Geostatistical Approaches to Characterization of Subsurface Contamination-17442. In 43rd Annual Waste Management Conference (WM2017).
[69] McMillan, H., Montanari, A., Cudennec, C., Savenije, H., Kreibich, H., Krueger, T., ... & Xia, J. (2016). Panta Rhei 2013–2015: global perspectives on hydrology, society and change. Hydrological Sciences Journal, 61(7), 1174-1191.
[70] Meerow, S., & Newell, J. P. (2017). Spatial planning for multifunctional green infrastructure: Growing resilience in Detroit. Landscape and urban planning, 159, 62-75.
[71] Mgbeahuruike, L. U. (2018). An investigation into soil pollution and remediation of selected polluted sites around the globe (Doctoral dissertation, Manchester Metropolitan University).
[72] Mitchell, M. (2012). Long-Term Monitoring and Maintenance Plan for the Mixed Waste Landfill March 2012 (No. SAND2012-1957P). Sandia National Lab.(SNL-NM), Albuquerque, NM (United States).
[73] Naghibi, S. A., Pourghasemi, H. R., & Dixon, B. (2016). GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental monitoring and assessment, 188(1), 44.
[74] Nashwan, M. S., Shahid, S., Chung, E. S., Ahmed, K., & Song, Y. H. (2018). Development of climate-based index for hydrologic hazard susceptibility. Sustainability, 10(7), 2182.
[75] Nelitz, M., Boardley, S., & Smith, R. (2013). Tools for climate change vulnerability assessments for watersheds. Prepared by ESSA Technologies Ltd. for the Canadian Council of Ministers of the Environment.
[76] Oni, O., Adeshina, Y. T., Iloeje, K. F., & Olatunji, O. O. (2018). Artificial Intelligence Model Fairness Auditor For Loan Systems. Journal ID, 8993, 1162.
[77] Osabuohien, F. O. (2017). Review of the environmental impact of polymer degradation. Communication in Physical Sciences, 2(1).
[78] Park, Y., Ligaray, M., Kim, Y. M., Kim, J. H., Cho, K. H., & Sthiannopkao, S. (2016). Development of enhanced groundwater arsenic prediction model using machine learning approaches in Southeast Asian countries. Desalination and Water Treatment, 57(26), 12227-12236.
[79] Parker, B. L., Cherry, J. A., & Chapman, S. W. (2012). Discrete fracture network approach for studying contamination in fractured rock. AQUA mundi, 3(2), 101-116.
[80] Perra, E., Piras, M., Deidda, R., Paniconi, C., Mascaro, G., Vivoni, E. R., ... & Meyer, S. (2018). Multimodel assessment of climate change-induced hydrologic impacts for a Mediterranean catchment. Hydrology and Earth System Sciences, 22(7), 4125-4143.
[81] Phenrat, T., Otwong, A., Chantharit, A., & Lowry, G. V. (2016). Ten-year monitored natural recovery of lead-contaminated mine tailing in Klity Creek, Kanchanaburi Province, Thailand. Environmental Health Perspectives, 124(10), 1511.
[82] Provoost, J., Tillman, F., Weaver, J., Reijnders, L., Bronders, J., Van Keer, I., & Swartjes, F. (2013). Vapour intrusion into buildings–aliterature review. Soil contamination and indoor air quality, 15.
[83] Ransom, K. M., Nolan, B. T., Traum, J. A., Faunt, C. C., Bell, A. M., Gronberg, J. A. M., ... & Harter, T. (2017). A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA. Science of the Total Environment, 601, 1160-1172.
[84] Ribeiro Neto, A., Scott, C. A., Lima, E. A., Montenegro, S. M. G. L., & Cirilo, J. A. (2014). Infrastructure sufficiency in meeting water demand under climate-induced socio-hydrological transition in the urbanizing Capibaribe River basin–Brazil. Hydrology and Earth System Sciences, 18(9), 3449-3459.
[85] Rodriguez-Galiano, V., Mendes, M. P., Garcia-Soldado, M. J., Chica-Olmo, M., & Ribeiro, L. (2014). Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain). Science of the Total Environment, 476, 189-206.
[86] Roghani, M. (2018). Investigation of Volatile Organic Compounds (VOCs) Detected at Vapor Intrusion sites.
[87] Sayles, L. R. (2017). Managing large systems: organizations for the future. Routledge.
[88] Schaltegger, S., & Csutora, M. (2012). Carbon accounting for sustainability and management. Status quo and challenges. Journal of cleaner production, 36, 1-16.
[89] Scheidt, C., Li, L., & Caers, J. (Eds.). (2018). Quantifying uncertainty in subsurface systems. John Wiley & Sons.
[90] Schultz, G. A., & Engman, E. T. (Eds.). (2012). Remote sensing in hydrology and water management. Springer Science & Business Media.
[91] Sims, N. C., & Colloff, M. J. (2012). Remote sensing of vegetation responses to flooding of a semi-arid floodplain: Implications for monitoring ecological effects of environmental flows. Ecological Indicators, 18, 387-391.
[92] Singh, K. P., Gupta, S., & Mohan, D. (2014). Evaluating influences of seasonal variations and anthropogenic activities on alluvial groundwater hydrochemistry using ensemble learning approaches. Journal of Hydrology, 511, 254-266.
[93] Singh, R., van Werkhoven, K., & Wagener, T. (2014). Hydrological impacts of climate change in gauged and ungauged watersheds of the Olifants basin: a trading-space-for-time approach. Hydrological Sciences Journal, 59(1), 29-55.
[94] Sorooshian, S., Nguyen, P., Sellars, S., Braithwaite, D., AghaKouchak, A., & Hsu, K. (2014). Satellite-based remote sensing estimation of precipitation for early warning systems. Extreme natural hazards, disaster risks and societal implications, 1, 99.
[95] Steininger, K. W., Lininger, C., Meyer, L. H., Muñoz, P., & Schinko, T. (2016). Multiple carbon accounting to support just and effective climate policies. Nature Climate Change, 6(1), 35-41.
[96] Sweeney, M. W., & Kabouris, J. C. (2017). Modeling, instrumentation, automation, and optimization of water resource recovery facilities. Water Environment Research, 89(10), 1299-1314.
[97] Tang, Q., & Luo, L. (2014). Carbon management systems and carbon mitigation. Australian Accounting Review, 24(1), 84-98.
[98] Thakur, J. K., Singh, S. K., & Ekanthalu, V. S. (2017). Integrating remote sensing, geographic information systems and global positioning system techniques with hydrological modeling. Applied Water Science, 7(4), 1595-1608.
[99] Turczynowicz, L., Pisaniello, D., & Williamson, T. (2012). Health risk assessment and vapor intrusion: A review and Australian perspective. Human and Ecological Risk Assessment: An International Journal, 18(5), 984-1013.
[100] Viviroli, D., Archer, D. R., Buytaert, W., Fowler, H. J., Greenwood, G. B., Hamlet, A. F., ... & Woods, R. (2011). Climate change and mountain water resources: overview and recommendations for research, management and policy. Hydrology and Earth System Sciences, 15(2), 471-504.
[101] Wagesho, N. (2014). Catchment dynamics and its impact on runoff generation: coupling watershed modelling and statistical analysis to detect catchment responses. International Journal of Water Resources and Environmental Engineering, 6(2), 73-87.
[102] Wang, H., Cai, Y., Tan, Q., & Zeng, Y. (2017). Evaluation of groundwater remediation technologies based on fuzzy multi-criteria decision analysis approaches. Water, 9(6), 443.
[103] Wang, X., Unger, A. J., & Parker, B. L. (2014). Risk-Based Characterization for Vapour Intrusion at a Conceptual Brownfields Site: Part 2. Pricing the Risk Capital. Journal of Civil Engineering, 3(4), 189-208.
[104] Watts, G., Battarbee, R. W., Bloomfield, J. P., Crossman, J., Daccache, A., Durance, I., ... & Wilby, R. L. (2015). Climate change and water in the UK–past changes and future prospects. Progress in Physical Geography, 39(1), 6-28.
[105] Williamson, M. (2011). Advanced simulation capability for environmental management (ASCEM): An overview of.
[106] Williamson, M., Meza, J., Moulton, D., Gorton, I., Freshley, M., Dixon, P., ... & Collazo, Y. T. (2011). Advanced simulation capability for environmental management (ASCEM): an overview of initial results. Technology & Innovation, 13(2), 175-199.
[107] Yaron, B., Dror, I., & Berkowitz, B. (2012). Soil-subsurface change: chemical pollutant impacts. Springer Science & Business Media.
[108] Zeidan, B. A. (2017). Groundwater degradation and remediation in the Nile Delta Aquifer. In The Nile Delta (pp. 159-232). Cham: Springer International Publishing.
[109] Zhai, X., Yue, P., & Zhang, M. (2016). A sensor web and web service-based approach for active hydrological disaster monitoring. ISPRS International Journal of Geo-Information, 5(10), 171.
[110] Zhang, Y., Peng, C., Li, W., Fang, X., Zhang, T., Zhu, Q., ... & Zhao, P. (2013). Monitoring and estimating drought-induced impacts on forest structure, growth, function, and ecosystem services using remote-sensing data: recent progress and future challenges. Environmental Reviews, 21(2), 103-115.
How to cite this paper
@article{1713077,
author = {Omolola Badmus, Azeez Lamidi Olamide},
title = {Data-Driven Framework for Predicting Subsurface Contamination Pathways in Complex Remediation Projects},
journal = {Iconic Research And Engineering Journals},
year = {2018},
volume = {2},
number = {5},
pages = {312-335},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713077.pdf},
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.},
month = {November},
}