Sayeed M. 2005. Hybrid genetic algorithm-local search methods for solving groundwater source identifcation inverse problems. Journal of Water Resources Planning & Management,131(1): 45-57. |
model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems; documentation and user's guide. Alabama University. |
CHEN Yan, Oliver Dean S. 2013. LevenbergMarquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification. Computational Geosciences, 17(4): 689-703. |
Mcdonald M G, Harbaugh A W. 1988. MODFLOW, A modular three-dimensional finite difference groundwater flow model, OpenFile Report 83-875, Chapter A1. |
Snodgrass M F, Kitanidis P K. 1997. A geostatistical approach to contaminant source identification. Water Resources Research, 33(4): 329-335. |
Prakash O, Datta B. 2015. Optimal characterization of pollutant sources in contaminated aquifers by integrating sequential-monitoringnetwork design and source identification: Methodology and an application in Australia. Hydrogeology J, 23(6): 1089-1107. |
CHANG Hai-bin, LIAO Qin-zhuo, ZHANG Dongxiao. 2017. Surrogate model based iterative ensemble smoother for subsurface flow data assimilation. Advances in Water Resources, 100: 96-108. |
Butera I, Tanda M G, Zanini A. 2013. Simultaneous identifcation of the pollutant release history and the source location in groundwater by means of a geostatistical approach. Stochastic Environmental Research and Risk Assessment, 27(5): 1269-1280. |
Ayvaz M T. 2016. A hybrid simulation-optimization approach for solving the areal groundwater pollution source identification problems. Hydrology Journal, 538: 161-176. |
ZHENG C, WANG P P. 1999. MT3DMS: A modular three-dimensional multispecies transport |
Guneshwor L, Eldho T I, Kumar A V. 2018. Identification of groundwater contamination sources using meshfree RPCM simulation and particle swarm optimization. Water Resources Management, 32(4): 1517-1538. |
Emerick A A, Reynolds A C. 2012. History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations. Computational Geosciences, 16(3): 639-665. |
Srivastava D, Singh R M. 2014. Breakthrough curves characterization and identification of an unknown pollution source in groundwater system using an artificial neural network (ANN). Environmental Forensics, 15(2): 175-189. |
JU Lei, ZHANG Jian-jiang, MENG Long, et al. 2018. An adaptive Gaussian processbased iterative ensemble smoother for data assimilation. Advances in Water Resources, 115: 125-135. |
GU Wen-long, LU Wen-xi, ZHANG Yu, et al. 2016. Reconstructing the release history of groundwater contamination sources based on the Bayesian inference and improved MCMC method. Journal of Hydraulic Engineering, 47(6): 772-779. |
LI Liang-ping, Stetler Larry, CAO Zhen-dan, et al. 2018. An iterative normal-score ensemble smoother for dealing with non-Gaussianity in data assimilation. Journal of Hydrology, 567: 759-766. |
XU Teng, Jaime Gómez-Hernández J. 2018. Simultaneous identification of a contaminant source and hydraulic conductivity via the restart normal-score ensemble Kalman flter. Advances in Water Resources, 112: 106-123. |
ZHANG Jian-jiang, LIN Guang, LI Wei-xuan, et al. 2018. An iterative local updating ensemble smoother for estimation and uncertainty assessment of hydrologic model parameters with multimodal distributions.Water Resources Research, 54: 1716-1733. |
JIANG Si-min, ZHANG Ya-li, WANG Pei, et al. 2013. An almost-parameter-free harmony search algorithm for groundwater pollution source identification. Water Science and Technology, 68(11): 2359-2366. |
ZHANG Dong-xiao, LU Zhi-ming. 2014. An efcient, high-order perturbation approach for ?ow in random porous media via KarhunenLoève and polynomial expansions. Journal of Computational Physics, 194(2): 773-794. |
WU Jian-feng, ZHENG Chun-miao. 2004. Contaminant monitoring network design: Recent advances and future directions. Advances in Earth Science, 19(3): 429-436. |