Abstract
The simulation optimization method was used to the identification of light nonaqueous phase liquid (LNAPL) groundwater contamination source (GCS) with the help of a hypothetical case in this study. When applying the simulation optimization method to identify GCS, it was a common technical means to establish surrogate model for the simulation model to participate in the iterative calculation to reduce the calculation load and calculation time. However, it was difficult for a single modeling method to establish surrogate model with high accuracy for the LNAPL contamination multiphase flow simulation model (MFSM). To give full play to advantages of single surrogate model and improve the accuracy of the surrogate model to the MFSM, a combination of deep belief neural network (DBNN) and long short-term memory (LSTM) neural network was used to establish artificial intelligence ensemble surrogate model (AIESM) for the MFSM. At the same time, to reduce the influence of noise in observed concentrations on the accuracy of the identification results, empirical mode decomposition (EMD) and wavelet analysis methods were used to denoise the observed concentrations, and their noise reduction effects were compared. The observed concentrations with better noise reduction effect and the observed concentrations without denoising were used to construct the objective function, and constraints of the optimization model were determined meanwhile. Then, the objective function and the constraints were integrated to build the optimization model to identify GCS and simulation model parameters. Applying the AIESM instead of the MFSM to embed in the optimization model and participate in the iterative calculation. Finally, the genetic algorithm (GA) was used to solve the optimization model to obtain the identification results of GCS and simulation model parameters. The results showed that compared with the single DBNN and LSTM surrogate models, AIESM obtained the highest accuracy and could replace the MFSM to participate in the iterative calculation, thereby reducing the calculation load and calculation time by more than 99%. Comparing with the wavelet analysis, EMD could reduce the noise in the concentrations more effectively, improved the accuracy of the approximated concentrations to the actual values, and increased the accuracy of the GCSs identification results by 1.45%.
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References
Aral MM, Guan J, Maslia ML (2001) Identification of contaminant source location and release history in aquifers. J Hydrol Eng 6(3):225–234. https://doi.org/10.1061/(ASCE)1084-0699(2001)6:3(225)
Aksoy M (2010) Benzene as a leukemogenic and carcinogenic agent. Am J Ind Med 8(1):9–20. https://doi.org/10.1002/ajim.4700080103
Asher MJ, Croke BFW, Jakeman AJ et al (2015) A review of surrogate models and their application to groundwater modeling. Water Resour Res 51(8):5957–5973. https://doi.org/10.1002/2015WR016967
Ayvaz MT (2016) A hybrid simulation–optimization approach for solving the areal groundwater pollution source identification problems. J Hydrol 538:161–176. https://doi.org/10.1016/j.jhydrol.2016.04.008
Chen H, Wan GX, Xiao ZJ (2017) Intrusion detection method of deep belief network model based on optimization of data processing. J Comput Appl 37(6):1636–1643
Chen Z, Gomez-Hernandez JJG, Xu T et al (2018) Joint identification of contaminant source and aquifer geometry in a sandbox experiment with the restart Ensemble Kalman filter. J Hydrol 564:1074–1084. https://doi.org/10.1016/j.jhydrol.2018.07.073
Daubechies I, Heil C (1992) Ten lectures on wavelets. Soc Ind 10(1063/1):4823127
De Blanc PC (1998) Development and demonstration of a biodegradationmodel for non-aqueous phase liquids in groundwater, Ph.D. dissertation, The University of Texas, Austin
Dokou Z, Pinder GF (2009) Optimal search strategy for the definition of a DNAPL source. J Hydrol 376(3–4):542–556. https://doi.org/10.1016/j.jhydrol.2009.07.062
Datta B, Chakrabarty D, Dhar A (2009) Simultaneous identification of unknown groundwater pollution sources and estimation of aquifer parameters. J Hydrol 376(1 2):48–57. https://doi.org/10.1016/j.jhydrol.2009.07.014
Gorelick SM, Evans B, Remson I (1983) Identifying sources of groundwater pollution: an optimization approach. Water Resour Res 19:779–790
Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5–6):602–610. https://doi.org/10.1016/j.neunet.2005.06.042
Graves A, Mohamed AR, Hinton G (2013) Speech recognition with deep recurrent neural networks. 2013 IEEE International Conference On Acoustics, Speech and Signal Processing (ICASSP), pp 6645–6649
Guo JY, Lu WX, Yang QC et al (2019) The application of 01 mixed integer nonlinear programming optimization model based on a surrogate model to identify the groundwater pollution source. J Contam Hydrol 220:18–25. https://doi.org/10.1016/j.jconhyd.2018.11.005
Hecht-Nielsen R (1989) Theory of the backpropagation neural network [C] Neural Networks IJCNN. Int Joint Conf. https://doi.org/10.1109/IJCNN.1989.118638
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A: Math Phys Eng Sci 454(1971):903–995. https://doi.org/10.1098/rspa.1998.0193
Huntley D, Beckett GD (2002) Persistence of LNAPL sources: relationship between risk reduction and LNAPL recovery. J Contam Hydrol 59(1–2):3–26. https://doi.org/10.1016/S0169-7722(02)00073-6
Hinton GE, Osindero S, The YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554. https://doi.org/10.1016/j.jhydrol.2021.126670
Hinton G (2010) A practical guide to training restricted Boltzmann machines. Momentum 9(1):926–947
Hou ZY, Lu WX, Chu HB et al (2015) Selecting parameter-optimized surrogate models in DNAPL-contaminated aquifer remediation strategies. Environ Eng Sci 32(12):1016–1026. https://doi.org/10.1089/ees.2015.0055
Hou ZY, Lu WX (2018) Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites. Hydrogeol J 26(3):923–932. https://doi.org/10.1007/s10040-017-1690-1
Hou ZY, Lao WM, Wang Y et al (2021) (2021) Cyclic feedback updating approach and uncertainty analysis for the source identification of DNAPL-contaminated aquifers. J Water Resour Plan Manag 147(2):04020103. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001322
Johnston CD, Trefry MG (2009) Characteristics of light nonaqueous phase liquid recovery in the presence of fine-scale soil layering. Water Resour Res 45:W05412. https://doi.org/10.1029/2008WR007218
Jha MK, Datta B (2011) Simulated annealing based simulation-optimization approach for identification of unknown contaminant sources in groundwater aquifers. Desalin Water Treat 32:79–85. https://doi.org/10.5004/dwt.2011.2681
Jiang X, Ma R, Wang Y et al (2021) Two-stage surrogate model-assisted Bayesian framework for groundwater contaminant source identification. J Hydrol 594(1–2):125955. https://doi.org/10.1016/j.jhydrol.2021.125955
Liu C, Ball WP (1999) Application of inverse methods to contaminant source identification from aquitard diffusion profiles at Dover AFB. Delaware Water Resour Res 35(7):1975–1985. https://doi.org/10.1029/1999wr900092
Luo JN, Lu WX (2014) A mixed-integer non-linear programming with surrogate model for optimal remediation design of NAPLs contaminated aquifer. Int J Environ Pollut 54(1):1–16. https://doi.org/10.1504/IJEP.2014.064047
Li JH, Lu WX, Wang H et al (2020) Groundwater contamination sources identification based on kernel extreme learning machine and its effect due to wavelet denoising technique. Environ Sci Pollut Res 27(27):34107–34120. https://doi.org/10.1007/s11356-020-08996-7
Li JH, Lu WX, Luo JN (2021) Groundwater contamination sources identification based on the long-short term memory network. J Hydrol 601(1):126670. https://doi.org/10.1016/j.jhydrol.2021.126670
Mahar PS, Datta B (1997) Optimal monitoring network and ground-water–pollution source identification. J Water Resour Plan Manag 123(4):199–207. https://doi.org/10.1061/(ASCE)0733-9496(1997)123:4(199)
Mahar PS, Datta B (2000) Identification of pollution sources in transient groundwater systems. Water Resour Manage 14(3):209–227. https://doi.org/10.1023/A:1026527901213
Mahar PS, Datta B (2001) Optimal identification of ground-water pollution sources and parameter estimation. J Water Resour Plan Manag 127(1):20–29. https://doi.org/10.1061/(ASCE)0733-9496(2001)127:1(20)
Mo SX, Nicholas Z, Shi XQ et al (2019) Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identification. Water Resour Res 55(5):3856–3881. https://doi.org/10.1029/2018WR024638
Moghaddam MB, Mazaheri M, Samani JMV (2021) Inverse modeling of contaminant transport for pollution source identification in surface and groundwaters: a review. Groundw Sustain Dev 12(2021):100651. https://doi.org/10.1016/j.gsd.2021.100651
Miao TS, Guo JY (2021) Application of artificial intelligence deep learning in numerical simulation of seawater intrusion. Environ Sci Pollut Res 28(38):54096–54104. https://doi.org/10.1007/s11356-021-13680-5
Queipo NV, Haftka RT, Shyy W et al (2005) Surrogate based analysis and optimization. Prog Aerosp Sci 41(1):1–28. https://doi.org/10.1016/j.paerosci.2005.02.001
Rohmer J, Foerster E (2011) Global sensitivity analysis of large-scale numerical landslide models based on Gaussian-Process meta-modeling. Comput Geosci 37(7):917–927. https://doi.org/10.1016/j.cageo.2011.02.020
Skaggs TH, Kabala ZJ (1994) Recovering the release history of a groundwater contaminant. Water Resour Res 30(1):71–79. https://doi.org/10.1029/93WR02656
Singh RM, Datta B, Jain A (2004) Identification of unknown groundwater pollution sources using artificial neural networks. J Water Resour Plan Anagement 130(6):506–514. https://doi.org/10.1061/(ASCE)0733-9496(2004)130:6(506)
Sookhak LK, Rayner JL, Davis GB (2019) Towards optimizing LNAPL remediation. Water Resour Res 55(2):923–936. https://doi.org/10.1029/2018WR023380
Tomlinson DW, Rivett M, Wealthall GP et al (2017) Understanding complex LNAPL sites: illustrated handbook of LNAPL transport and fate in the subsurface. J Environ Manage 2:748–756. https://doi.org/10.1016/j.jenvman.2017.08.015
Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85
Woodbury AD, Ulrych TJ (1996) Minimum relative entropy inversion: theory and application to recovering the release history of a groundwater contaminant. Water Resour Res 32(9):2671–2681. https://doi.org/10.1029/95WR03818
Wu Z, Huang NE (2004) A study of the characteristics of white noise using the empirical mode decomposition method. Proc Math Phys Eng Sci 460(2046):1597–1611. https://doi.org/10.1098/rspa.2003.1221
Wang H, Lu WX, Chang ZB (2021) Simultaneous identification of groundwater contamination source and aquifer parameters with a new weighted–average wavelet variable–threshold denoising method. Environ Sci Pollut Res 28(8):1–16. https://doi.org/10.1007/s11356-021-12959-x
Yeh HD, Chang TH, Lin YC (2007) Groundwater contaminant source identification by a hybrid heuristic approach. Water Resour Res 43(9): https://doi.org/10.1029/2005WR004731
Zhang JJ, Li WX, Zeng LZ et al (2016) An adaptive Gaussian process-based method for efficient Bayesian experimental design in groundwater contaminant source identification problems. Water Resour Res 52(8):5971–5984. https://doi.org/10.1002/2016WR018598
Zhao Y, Lu WX, Xiao CN (2016) A Kriging surrogate model coupled in simulation–optimization approach for identifying release history of groundwater sources. J Contam Hydrol 185:51–60. https://doi.org/10.1016/j.jconhyd.2016.01.004
Zhao Y, Fu Q, Lu WX et al (2019) Wavelet denoising and cubic spline interpolation for observation data in groundwater pollution source identification problems. Water Sci Technol 19(5–6):1454–1462. https://doi.org/10.2166/ws.2019.013
Acknowledgements
Special gratitude is extended to the journal editors for their efforts in evaluating this study. The valuable comments provided by the anonymous reviewers are also gratefully acknowledged.
Funding
This work was supported by the National Nature Science Foundation of China (Grant Nos. 42202273), the Fundamental Research Funds for the Central Universities (Grant Nos. 2412022QD001) and the National Key R&D Program of China (Grant Nos. 2019YFC0409101).
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All authors contributed to the study conception and design. Conceptualization, methodology, software, writing–original draft, and formal analysis were performed by Jiuhui Li. Writing—review and editing, supervision, and project administration were performed by Zhengfang Wu. Methodology, validation, writing–review, and editing were performed by Hongshi He. Software, validation, and project administration were performed by Wenxi Lu. All authors read and approved the final manuscript.
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Li, J., Wu, Z., He, H. et al. Identification of light nonaqueous phase liquid groundwater contamination source based on empirical mode decomposition and deep learning. Environ Sci Pollut Res 30, 38663–38682 (2023). https://doi.org/10.1007/s11356-022-24671-5
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DOI: https://doi.org/10.1007/s11356-022-24671-5