Evaluation of Unknown Groundwater Contaminant Sources Characterization Efficiency under Hydrogeologic Uncertainty in an Experimental Aquifer Site by Utilizing Surrogate Models — Oak Academic Publishing
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Evaluation of Unknown Groundwater Contaminant Sources Characterization Efficiency under Hydrogeologic Uncertainty in an Experimental Aquifer Site by Utilizing Surrogate Models
Discipline of Civil Engineering, College of Science and Engineering, James Cook University, Townsville, Australia
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CRC for Contamination Assessment and Remediation of the Environment, CRC CARE, University of Newcastle, Callaghan, Australia
1 Discipline of Civil Engineering, College of Science and Engineering, James Cook University, Townsville, Australia
2 CRC for Contamination Assessment and Remediation of the Environment, CRC CARE, University of Newcastle, Callaghan, Australia
Characterization of unknown groundwater contaminant sources is an important but difficult step in effective groundwater management. The difficulties arise mainly due to the time of contaminant detection which usually happens a long time after the start of contaminant source(s) activities. Usually, limited information is available which also can be erroneous. This study utilizes Self-Organizing Map (SOM) and Gaussian Process Regression (GPR) algorithms to develop surrogate models that can approximate the complex flow and transport processes in a contaminated aquifer. The important feature of these developed surrogate models is that unlike the previous methods, they can be applied independently of any linked optimization model solution for characterizing of unknown groundwater contaminant sources. The performance of the developed surrogate models is evaluated for source characterization in an experimental contaminated aquifer site within the heterogeneous sand aquifer, located at the Botany Basin, New South Wales, Australia. In this study, the measured contaminant concentrations and hydraulic conductivity values are assumed to contain random errors. Simulated responses of the aquifer to randomly specified contamination stresses as simulated by using a three-dimensional numerical simulation model are utilized for initial training of the surrogate models. The performance evaluation results obtained by using different surrogate models are also compared. The evaluation results demonstrate the different capabilities of the developed surrogate models. These capabilities lead to development of an efficient methodology for source characterization based on utilizing the trained and tested surrogate models in an inverse mode. The obtained results are satisfactory and show the potential applicability of the SOM and GPR-based surrogate models for unknown groundwater contaminant source characterization in an inverse mode.
Mahar, P.S. and Datta, B. (2000) Identification of Pollution Sources in Transient Groundwater Systems. Water Resources Management, 14, 19.
Datta, B. and Kourakos, G. (2015) Preface: Optimization for Groundwater Characterization and Management. Hydrogeology Journal, 23, 7. https://doi.org/10.1007/s10040-015-1297-3
Bagtzoglou, A.C., Dougherty, D.E. and Tompson, A.F.B. (1992) Application of Particle Methods to Reliable Identification of Groundwater Pollution Sources. Water Resources Management, 6, 9.
Woodbury, A.D. and Ulrych, T.J. (1996) Minimum Relative Entropy Inversion: Theory and Application to Recovering the Release History of a Groundwater Contaminant. Water Resources Research, 32, 2671-2681. https://doi.org/10.1029/95WR03818
Liu, C.X. and Ball, W.P. (1999) Application of Inverse Methods to Contaminant Source Identification from Aquitard Diffusion Profiles at Dover AFB, Delaware. Water Resources Research, 35, 1975-1985. https://doi.org/10.1029/1999WR900092
Atmadja, J. and Bagtzoglou, A.C. (2001) Pollution Source Identification in Heterogeneous Porous Media. Water Resources Research, 37, 2113-2125. https://doi.org/10.1029/2001WR000223
Amirabdollahian, M. and Datta, B. (2013) Identification of Contaminant Source Characteristics and Monitoring Network Design in Groundwater Aquifers: An Overview. Journal of Environmental Protection, 4, 16. https://doi.org/10.4236/jep.2013.45A004
Gorelick, S.M., Evans, B. and Remson, I. (1983) Identifying Sources of Groundwater Pollution—An Optimization Approach. Water Resources Research, 19, 779-790. https://doi.org/10.1029/WR019i003p00779
Mahar, P.S. and Datta, B. (1997) Optimal Monitoring Network and Ground-Water-Pollution Sources Identification. Journal of Water Resource Planning and Management, 123, 199. https://doi.org/10.1061/(ASCE)0733-9496(1997)123:4(199)
Mahar, P.S. and Datta, B. (2001) Optimal Identification of Ground-Water Pollution Sources and Parameter Estimation. Journal of Water Resources Planning and Management-ASCE, 127, 10. https://doi.org/10.1061/(ASCE)0733-9496(2001)127:1(20)
Das, A. and Datta, B. (1999) Development of Multiobjective Management Models for Coastal Aquifers. Journal of Water Resources Planning and Management, 125, 12. https://doi.org/10.1061/(ASCE)0733-9496(1999)125:2(76)
Aral, M.M., Guan, J.B. and Maslia, M.L. (2001) Identification of Contaminant Source Location and Release History in Aquifers. Journal of Hydrologic Engineering, 6, 225-234. https://doi.org/10.1061/(ASCE)1084-0699(2001)6:3(225)
Singh, R.M. and Datta, B. (2006) Identification of Groundwater Pollution Sources using GA-Based Linked Simulation Optimization Model. Journal of Hydrologic Engineering, 11, 9. https://doi.org/10.1061/(ASCE)1084-0699(2006)11:2(101)
Singh, R.M., Datta, B. and Jain, A. (2004) Identification of Unknown Groundwater Pollution Sources using Artificial Neural Networks. Journal of Water Resources Planning and Management-Asce, 130, 9. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:6(506)
Singh, R.M. and Datta, B. (2007) Artificial Neural Network Modeling for Identification of Unknown Pollution Sources in Groundwater with Partially Missing Concentration Observation Data. Water Resources Management, 21, 557-572. https://doi.org/10.1007/s11269-006-9029-z
Jha, M. and Datta, B. (2012) Simulated Annealing Based Simulation-Optimization Approach for Identification of Unknown Contaminant Sources in Groundwater Aquifers. 3rd International Conference on Challenges in Environmental Science & Engineering, Cairns.
Prakash, O. and Datta, B. (2014) Characterization of Groundwater Pollution Sources with Unknown Release Time History. Journal of Water Resource and Protection, 6, 14. https://doi.org/10.4236/jwarp.2014.64036
Prakash, O. and Datta, B. (2014) Optimal Monitoring Network Design for Efficient Identification of Unknown Groundwater Pollution Sources. International Journal of GEOMATE, 6, 785-790.
Prakash, O. and Datta, B. (2015) Optimal Characterization of Pollutant Sources in Contaminated Aquifers by Integrating Sequential-Monitoring-Network Design and Source Identification: Methodology and an Application in Australia. Hydrogeology Journal, 23, 1089-1107. https://doi.org/10.1007/s10040-015-1292-8
Jha, M. and Datta, B. (2013) Three-Dimensional Groundwater Contamination Source Identification using Adaptive Simulated Annealing. Journal of Hydrologic Engineering, 18, 307-317. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000624
Amirabdollahian, M. and Datta, B. (2014) Identification of Pollutant Source Characteristics under Uncertainty in Contaminated Water Resources Systems using Adaptive Simulated Anealing and Fuzzy Logic. International Journal of GEOMATE, 6, 757-762.
Beck, P.H. (2000) Transport of Conservative and Reactive Inorganic Elements in the Saturated Part of a Hetrogeneous Sand Aquifer, Botany Basin, Sydney, Australia. University of New South Wales, Sydney.
Harbaugh, A.W. (2005) MODFLOW-2005, The U.S. Geological Survey Modular Ground-Water Model—The Ground-Water Flow Process. U.S. Geological Survey Techniques and Methods 6-A16.
Zheng, C. and Wang, P.P. (1999) MT3DMS: A Modular Three-Dimensional Multispecies Transport Model for Simulation of Advection, Dispersion, and Chemical Reactions of Contaminants in Groundwater Systems; Documentation and User’s Guide. US Army Corps of Engineers-Engineer Research and Development Center, Contract Report SERDP-99-1, 220.
Gorissen, D., et al. (2010) A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design. Journal of Machine Learning Research, 11, 5.
Razavi, S., Tolson, B.A. and Burn, D.H. (2012) Review of Surrogate Modeling in Water Resources. Water Resources Research, 48, 32. https://doi.org/10.1029/2011WR011527
Wang, C., et al. (2014) An Evaluation of Adaptive Surrogate Modeling Based Optimization with Two Benchmark Problems. Environmental Modelling & Software, 60, 167-179. https://doi.org/10.1016/j.envsoft.2014.05.026
Queipo, N.V., et al. (2005) Surrogate-Based Analysis and Optimization. Progress in Aerospace Sciences, 41, 1-28. https://doi.org/10.1016/j.paerosci.2005.02.001
Kohenon, T., et al. (1996) Engineering Applications of the Self-Organizing Map. IEEE, 84, 1358-1384. https://doi.org/10.1109/5.537105
Di Mauro, M., et al. (2016) Design Performance Analysis of a Self-Organizing Map for Statistical Monitoring of Distribution-Free Data Streams. CIRP CMS, 6.
Hazrati, Y.S. and Datta, B. (2017) Adaptive Surrogate Model Based Optimization (ASMBO) for Unknown Groundwater Contaminant Source Characterizations using Self-Organizing Maps. Journal of Water Resource and Protection, 9, 23.
Chalasani, R. and Principe, J.C. (2015) Self-Organizing Maps with Information Theoretic Learning. Neurocomputing, 147, 12. https://doi.org/10.1016/j.neucom.2013.12.059
Amauri, H., et al. (2015) Regional Models: A New Approach for Nonlinear System Identification via Clustering of the Self-Organizing Map. Neurocomputing, 147, 16.
Vesanto, J., et al. (2000) SOM Toolbox for Matlab 5. Report A57, 60. http://www.cis.hut.fi/projects/somtoolbox
Belyaev, M., et al. (2016) GTApprox: Surrogate Modeling for Industrial Design. 31.
Schulz, E., Speekenbrink, M. and Krause, A. (2016) A Tutorial on Gaussian Process Regression with a Focus on Exploration-Exploitation Scenarios. 37.
Retherford, J.Q. and McDonald, M. (2010) Estimation and Validation of Gaussian Process Surrogate Models for MEPDG-Based Sensitivity Analysis and Design Optimization. Vanderbilt University: Transportation Research Board Annual Meeting.
Jankowski, J. and Beck, P. (2010) Aquifer Heterogeneity: Hydrogeological and Hydrochemical Properties of the Botany Sands Aquifer and Their Impact on Contaminant Transport. Australian Journal of Earth Sciences, 47, 20.
Amir Abdollahian, M. (2016) Development of Integrated Methodologies for Optimal Monitoring and Source Characterization in Contaminated Groundwater Systems under Uncertainty. College of Science and Engineering, James Cook University.
Boman, G.K., Molz, F.J. and Guven, O. (1995) An Evaluation of Interpolation Methodologies for Generating Three-Dimensional Hydraulic Property Distribution from Measured Data. Ground Water, 33, 12. https://doi.org/10.1111/j.1745-6584.1995.tb00279.x
Hazrati, Y.S. and Datta, B. (2017) Self-Organizing Map Based Surrogate Models for Contaminant Source Identification under Parameter Uncertainty. International Journal of GEOMATE, 13, 8.