Document Type : Original Article


Department of Water Resources Engineering, Faculty of Civil, Water, and Environmental Engineering, Shahid Beheshti University, Tehran, Iran



Introduction: Dense Nonaqueous Phase Liquids (DNAPL) are the most common types of groundwater pollution. Surfectant Enhanced Aquifer Remediation (SEAR) is one of the most common methods of DNAPL-contaminated aquifer remediation. Due to the high cost of the chemicals used in this method (surfectants or cosolvents), it is necessary to choose the appropriate wells pattern, and the optimal pumping rates. UTCHEM simulation software has the ability to model the fate and transport of DNAPL and the application of the SEAR method. The main problem with this software is the long time required to run multiple senarioes when using optimization algorithms. The purpose of this study is to use two machine learning methods (Artificial Neural Network and K nearest neighbor) as sorrugate simulation model and imbedding the best one into the LINGO software to optimize the SEAR method.
Material and methods: in the implementation of The SEAR method, the quantitative and qualitative aquifer data are required to model how to diffuse, transmit and delete DNAPLs in UTCHEM software. For this purpose information from the Camp Lejeune site in North Carolina, USA were used. In this study, by examining a variety of alternative models based on machine learning methods and implementing 250 different scenarios in UTCHEM software, two models, Artificial Neural Network (ANN) method, and k-nearest neighbors (KNN) were used to simulate the SEAR method and developing alternative model. In order to validate the two alternative models, 50 new scenarios were implemented in UTCHEM software and their percentage of removal was obtained. Also, using two alternative models, the percentage of removal of 50 scenarios were determined. in order to evaluate the performance of alternative models , the root mean square error (RMSE) was used and was compared with the results of other researchs. Finally an alternative model with more accuracy was used in LINGO software to optimize the Surfactant Enhanced Aquifer Remediation method (SEAR).
Results and discussion: RMSE values in the results obtained from alternative models ANN and KNN in the validation stage were 0.67 and 1.66 respectively, which indicates the high accuracy of both alternative models, especially ANN. The average run time of each UTCHEM software in this study was 45 minutes, while in the alternative model it was reduced to a few seconds; LINGO software also examined about 21,500 different scenarios in 30 minutes to determine the optimal scenario, while the time required for this task is more than 16,000 hours if the alternative model is not used. Based on the position and discharge of active wells in the optimum scenario, it was found that firstly the existing wells upstream and downstream of the Pollution have the most impact on the remediation and secondly, the time factor is more effective than the wells pumping discharge in the remediation. The optimized scenario obtained in this study remediates the DNAPL-contaminated area by up to 95% at a lower cost than the costs reported in the Camp Lejeune project over a period of 30 days.
Conclusion: Based on the results obtained in this study, it was found that the use of machine learning algorithms such as ANN and KNN, along with LINGO optimization software, which is one of the most powerful software for solving linear and nonlinear optimization problems, in addition to having the right accuracy, significantly reduces the time required to find the optimal scenario.


Araghinejad, S,. 2014. Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering. Springer Dordrecht Heidelberg New York London.
Battelle and Duke Engineering & Services (Battelle/DE&S), 2002. Surfactant-Enhanced Aquifer Remediation (SEAR) Design Manual, NFESC Technical Report TR-2206-ENV, Report Prepared for the Naval Facilities Engineering Service Center.
Bear, J. and Cheng, A. H. D., 2010. Modeling groundwater flow and contaminant transport . Springer Science & Business Media.
Brownlee, J., 2016. Master Machine Learning Algorithms: Discover how they Work and Implement them from Scratch (Machine Learning Mastery).
Camp Lejeuneet., 1999. FINAL Cost and Performance Report for Surfactant-Enhanced DNAPL Removal at Site 88, Marine Corps Base Camp Lejeune, North Carolina.
Delshad, M., Pope, G.A. and Sepehrnoori, K., 1996. A compositional simulator for modeling surfactant enhanced aquifer remediation, 1 formulation. J. Contam. Hydrol. 23, 303–327.
Foster, S., Hirata, R., Gomes, D., D’Elia, M. and Paris, M., 2002. Groundwater quality protection: a guide for water utilities, municipal authorities, and environment agencies. World Bank, Washington, DC.
Harun, S. and Ahmat, N.I. And Kassim, A. H. M., 2002. Artificial neural network model for RainfallRunoff Relationship. Journal Technology, 37(B) Dis. P. 1-12.
Hou, Z., Lu, W., Xue, H.  and Lin, J., 2017. A comparative research of different ensemble surrogate models based on set pair analysis for the DNAPL-contaminated aquifer remediation strategy optimization. Journal of Contaminant Hydrology.
Huling, S. G. and Weaver, J. W., 1991. Dense nonaqueous phase liquids. Superfund Technology Support Center for Ground Water, Robert S. Kerr Environmental Research Laboratory.
Jiang, X., Lu, W., Hou, Z., Zhao, H. and Na, J., 2015. Ensemble of surrogates-based optimization for identifying an optimal surfactant-enhanced aquifer remediation strategy at heterogeneous DNAPL-contaminated sites. Comput. Geosci. 84, 37–45.
Kueper, B.H. and McWhorter, D.B., 1991. The behavior of dense, nonaqueous phase liquids in fractured clay and rock. Ground Water 29, 716–728. j.1745-6584.1991.tb00563.x.
Kueper, B. H., Stroo, H. F., Vogel, C. M. and Ward, C. H. (Eds.)., 2014. Chlorinated solvent source zone remediation. Springer.
Lerner, D.N., Kueper, B.H. and Wealthall, G.P., 2003. An illustrated handbook of DNAPL transport and fate in the subsurface. Research Report. Environment Agency , Bristol, UK.
Lindo Concepts, 2022. LINGO 19.0 - Optimization Modeling Software for Linear, Nonlinear, and Integer Programming. Chicago, IL, 60642. USA.
Luo, J., Lu, W., Xin, X. and Chu, H., 2013. Surrogate model application to the identification of an optimal surfactant-enhanced aquifer remediation strategy for DNAPLcontaminated sites. Journal of Earth Science. 24, 1023–1032. 013-0395-1.
Luo, J. and Lu, W., 2014. Comparison of surrogate models with different methods in groundwater remediation process. Journal of earth system science.
Mayer, A. and Hassanizadeh, s. m., 2005. SOIL AND GROUNDWATER CONTAMINATION :NONAQUEOUS PHASE LIQUIDS-PRINCIPLES AND OBSERVATIONS. American Geophysical Union. Washington, DC.
Mayer, A. and Endres, K.L., 2007. Simultaneous optimization of dense non-aqueous phase liquid (DNAPL) source and contaminant plume remediation. J. Contam. Hydrol. 91, 288–311.
Menhaj, M. B., 2007. ”Fundamentals of Neural Networks”, Computational Intelligence, Vol. 1, Amirkabir university of technology, Tehran, Iran.
Mercer, J. W. and Cohen, R. M., 1990. A review of immiscible fluids in the subsurface: properties, models, characterization and remediation. Journal of Contaminant Hydrology.
Moradian, M.  and Sepehrifar, M., 2009. Improving the Accuracy of KNN Algorithm in Data Mining Using Dependency Laws, 15th Annual Computer Conference of Iranian Computer Association.
Nyer, E.K., 2009. Groundwater Treatment Technology. Published by Van Nostrand Reinhold ISBN 13: 9780442005627
Ouyang, Q., Lu, W., Lin, J., Deng, W. and Cheng, W., 2017a. Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites. Journal of Contaminant Hydrology.
Ouyang, Q., Lu, W., Miao, T., Deng, W., Jiang, C. and Luo, J., 2017b. Application of ensemble surrogates and adaptive sequential sampling to optimal groundwater remediation design at DNAPLs-contaminated sites. J. Contam. Hydrol. 207, 31–38. https://doi. org/10.1016/j.jconhyd.2017.10.007.
Qin, X.S., Huang, G.H., Chakma, A., Chen, B. and Zeng, G.M., 2007. Simulation-based process optimization for surfactant-enhanced aquifer remediation at heterogeneous DNAPL-contaminated sites. Sci. Total Environ. 381, 17–37. 10.1016/j.scitotenv.2007.04.011.
Qin, X. S., Huang, G. H. and He, L., 2009. Simulation and optimization technologies for petroleum waste management and remediation process control. Journal of Environmental Management.
Shaafi, E., 2013. Groundwater Rehabilitation System Design Optimization, Master Thesis in Civil Engineering - Water, Groundwater Orientation, December 2013.
Shams, R., Alimohammadi, S. and Yazdi, J., 2021. Optimizing surfactant-enhanced aquifer remediation based on Gaussian process surrogate model in DNAPL-contaminated sites considering different wells patterns. Groundwater for Sustainable Development.
Shmueli, G., Patel, N.R. and Bruce, P.C., 2011. Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner. Wiley.
Shobeyri, M., 2019. Optimum Design of Flood Control Levees, Combining Evolutionary Algorithm and Discrete Differential Dynamic Programming. Shahid Beheshti Univercity Faculty of Water & Environmental Engineering. feburay2019.
Suthersan, S.S., Horst, J., Schnobrich, M., Welty, N. and McDonough, J., 2016. Remediation Engineering, Remediation Engineering: Design Concepts, second ed. CRC Press.
Swikatek, J., Borzemski, L. and Wilimowska, Z., 2019. Information Systems Architecture and Technology: Proceedings of 40th Anniversary International Conference on Information Systems Architecture and Technology – ISAT 2019: Part II, Advances in Intelligent Systems and Computing. Springer International Publishing.