考虑参数和边界条件不确定性的地下水污染随机模拟

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Abstract In order to investigate the influences of random changes in model parameters and boundary conditions on the output uncertainty of groundwater solute transport model, combined application of Monte Carlo simulation and risk assessment were applied to illustrate the uncertainty analysis results of a hypothetical example. Firstly, a numerical simulation model of groundwater solute transport was established, then the parameters with greater impacts on the model output screened by local and global sensitivity analysis, together with the boundary conditions of the model (the first type of boundary conditions—head value) were set as random variables.Then the Gaussian Process Regression (GPR) method of optimizing hyperparameters was employed to establish an alternative model of the simulation model to complete the Monte Carlo stochastic simulation. Finally, statistical analysis and interval estimate of the results of random simulation were carried out, and the probability distribution function of pollutant concentration was used to estimate the risk of different degrees of pollution of observation wells 1, 2, and 3. The results show that when the confidence level was greater than 80%, the confidence intervals of the pollutant concentration values in observation wells 1, 2, and 3 were 34.77~35.03, 57.74~58.04, and 100.07~100.69mg/L, respectively. In addition, in observation wells 1, 2, and 3, the risk of slight pollution was 6%, 100% and 100%, respectively; the risk of moderate pollution was 0%, 0% and 99.6%, respectively; the risk of heavy pollution was 0%, 0%, and 0.5%, respectively. The present study can provide a reliable reference for pollution remediation and rational utilization of groundwater.

Key words： uncertainty of boundary conditions sensitivity analysis GPR substitution model uncertainty analysis risk assessment

Received: 14 December 2021

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X523

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	Articles by authors
	XU Ya-ning
	LU Wen-xi
	WANG Zi-bo
	JIA Shun-qing
	WANG Han
	PAN Zi-dong

Cite this article:

XU Ya-ning,LU Wen-xi,WANG Zi-bo等. Stochastic simulation of groundwater pollution considering uncertainty of parameters and boundary conditions[J]. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(7): 3244-3253.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2022/V42/I7/3244

[1]	张晓烨,董增川.地下水模拟模型与优化模型耦合技术研究进展[J].水北调与水利科技, 2012,10(2):142-144,149. Zhang X Y, Dong Z C. Research progress to couple groundwater simulation model and optimization model[J]. South-to-North Water Transfers and Water Science&Technology, 2012,10(2):142-144,149.
[2]	吴吉春,陆乐.地下水模拟不确定性分析[J].南京大学学报(自然科学版), 2011,47(3):227-234. Wu J C, Lu L. Uncertainty analysis for groundwater modeling[J]. Journal of Nanjing University (Natural Sciences), 2011,47(3):227-234.
[3]	顾文龙,卢文喜,马洪云,等.地下水数值模拟分析中降水入渗补给强度及渗透系数不确定性评价[J].水电能源科学, 2015,33(11):45-48,64. Gu W L, Lu W X, Ma H Y, et al. Uncertainty evaluation of precipitation infiltration rate and hydraulic conductivity in groundwater numerical simulation analysis[J]. Water Resources and Power, 2015,33(11):45-48,64.
[4]	束龙仓,王茂枚,刘瑞国,等.地下水数值模拟中的参数灵敏度分析[J].河海大学学报(自然科学版), 2007,35(5):491-495. Shu L C, Wang M M, Liu R G, et al. Sensitivity analysisofparametersin numerical simulation ofgroundwater[J]. Joumal of Hohai University (Natural Sciences), 2007,35(5):491-495.
[5]	张将伟,卢文喜,曲延光,等.基于Monte Carlo方法的地表水地下水耦合模拟模型不确定分析[J].水利学报, 2018,49(10):1254-1264. Zhang J W, Lu W X, Qu Y G, et al. Uncertainty analysis of surface water and groundwater coupling simulation model based on Monte Carlo method[J]. Journal of Hydraulic Engineering, 2018,49(10):1254-1264.
[6]	侯泽宇,王宇,卢文喜.地下水DNAPLs污染修复多相流模拟的替代模型[J].中国环境科学, 2019,39(7):2913-2920. Hou Z Y, Wang Y, Lu W X. Surrogate models of multi-phase flow simulation model for DNAPL-contaminated aquifer remediation[J]. China Environmental Science, 2019,39(7):2913-2920.
[7]	葛渊博,卢文喜,王梓博,等.基于BP神经网络替代模型的地下水污染随机模拟[J].中国农村水利水电, 2022,(3):107-113,119. Ge Y B, Lu W X, Wang Z B, el at. Random simulation of groundwater pollution based on BP neural network substitution model[J]. China Rural Water and Hydropower, 2022(3):107-113,119.
[8]	何志昆,刘光斌,赵曦晶,等.高斯过程回归方法综述[J].控制与决策, 2013,28(8):1121-1129,1137. He Z K, Liu G B, Zhao X J, el at. Overview of Gaussian process regression[J]. Control and Decision, 2013,28(8):1121-1129,1137.
[9]	刘宸博,邢帅,王丹菂,等.一种高斯过程回归的水体吸收系数反演模型[J].测绘科学技术学报, 2021,38(4):384-390. Liu C B, Xing S, Wang D D, el at. Absorption coefficients inversion model based on gaussian process regression[J]. Journal of Geomatics Science and Technology, 2021,38(4):384-390.
[10]	高烨,梁收运,王申宁,等.地下水数值模拟不确定性分析研究进展[J].地下水, 2020,42(1):28-31,97. Gao Y, Liang S Y, Wang S N, el at. Research progress on uncertainty analysis of groundwater numerical simulation[J]. Underground Water, 2020,42(1):28-31,97.
[11]	束龙仓,陶玉飞,刘佩贵.考虑水文地质参数不确定性的地下水补给量可靠度计算[J].水利学报, 2008,39(3):346-350. Shu L C, Tao Y F, Liu P G. Reliability calculation method for groundwater recharge in consideration of uncertainty of hydrogeological parameters[J]. Journal of Hydraulic Engineering, 2008,39(3):346-350.
[12]	Hassan A E, Bekhit H M, Chapman J B. Uncertainty assessment of a stochastic groundwater flow model using GLUE analysis[J]. Journal of Hydrology, 2008,362(1/2):89-109.
[13]	Hugman R, Stigter T Y, Monteiro J P, et al. Modeling the spatial and temporal distribution of coastal groundwater discharge for different water use scenarios under epistemic uncertainty:case study in South Portugal[J]. Environmental Earth Sciences, 2015,73(6):2657-2669.
[14]	李久辉,卢文喜,常振波,等.基于不确定性分析的地下水污染超标风险预警[J].中国环境科学, 2017,37(6):2270-2277. Li J H, Lu W X, Chang Z B, et al. Risk prediction of groundwater pollution based on uncertainty analysis[J]. China Environmental Science, 2017,37(6):2270-2277.
[15]	Na H N, Koo M H, Cha J H, et al. Sensitivity analysis of groundwater model predictions associated with uncertainty of boundary conditions:A case study[J]. Journal of Soil and Groundwater Environment, 2007,12(3):53-65.
[16]	邹立芝.数值法计算中边界条件处理方法的探讨[J].长春地质学院学报, 1992,(2):199-202. Zou L Z. Discussion on processing method of boundary conditions in numeracal calculation[J]. Journal of Changchun University of Earth Science, 1992,(2):199-202.
[17]	Atanassov E, Dimov I T. What Monte Carlo models can do and cannot do efficiently?[J]. Applied Mathematical Modelling, 2008,32(8):1477-1500.
[18]	Luo J, Lu W. Sobol sensitivity analysis of NAPL-contaminated aquifer remediation process based on multiple surrogates[J]. Computers&Geosciences, 2014,(67):110-116.
[19]	Nossent J, Elsen P, Bauwens W. Sobol'sensitivity analysis of a complex environmental model[J]. Environmental Modelling&Software, 2011,26(12):1515-1525.
[20]	欧阳琦,卢文喜,侯泽宇,等.基于替代模型的地下水溶质运移不确定性分析[J].中国环境科学, 2016,36(4):1119-1124. Ou Y Q, Lu W X, Hou Z Y, et al. Uncertainty analysis of groundwater solute transport based on surrogate model[J]. China Environmental Science, 2016,36(4):1119-1124.
[21]	Rasmussen C E, Williams C. Model Selection and Adaptation of Hyperparameters[C]. MIT Press, 2005.
[22]	谢建雄,鲁铁定.一种优化高斯过程回归的隧道围岩变形预测方法[J].测绘科学, 2021,46(4):50-56. Xie J X, Lu T D. A deformation prediction method for tunnel surrounding rock based on optimizing Gaussian process regression[J]. Science of Surveying and Mapping, 2021,46(4):50-56.
[23]	Rasmussen C E, Williams C. Gaussian Processes for Machine Learning[M]. Gaussian Processes for Machine Learning, 2005.
[24]	苏国韶,张研,张小飞.高斯过程机器学习方法在地下水位预测中的应用[J].中国农村水利水电, 2008,(12):48-50,54. Su G S, Zhang Y, Zhang X F. Application of Gaussian Process Machine Learning to the Prediction of Groundwater Level[J]. China Rural Water and Hydropower, 2008,(12):48-50,54.
[25]	叶婧.基于高斯过程回归的锂电池数据处理[D].北京:北京交通大学, 2016. Ye J. Research on Lithium-ion battery data processing based on Gaussian Process Regression[D]. Beijing:Beijing Jiaotong University, 2016.
[26]	Sundararajan S, Keerthi S S. Predictive approaches for choosing hyperparameters in Gaussian Processes[J]. Neural Comput, 2001, 13(5):1103-1118.
[27]	曹文梁,康岚兰.高斯过程回归超参数自适应选择粒子群优化算法[J].合肥工业大学学报(自然科学版), 2019,42(11):1479-1484. Cao W L, Kang L L. Particle swarm optimization for adaptive hyper-parameters acquisition of Gaussian process regression[J]. Journal of Hefei University of Technology (Natural Science), 2019, 42(11):1479-1484.
[28]	陈彦,吴吉春.含水层渗透系数空间变异性对地下水数值模拟的影响[J].水科学进展, 2005,16(4):482-487. Chen Y, Wu J C. Effect of the spatial variability of hydraulic conductivity in aquifer on the numerical simulation of groundwater[J]. Advances in Water Science, 2005,16(4):482-487.
[29]	林青,徐绍辉.基于GLUE方法的饱和多孔介质中溶质运移模型参数不确定性分析[J].水利学报, 2012,43(9):1017-1024. Lin Q, Xu S H. Parameter uncertainty analysis of solute transport in saturated porous media based on GLUE method[J]. Journal of Hydraulic Engineering, 2012,43(9):1017-1024.
[30]	王涵,卢文喜,李久辉,等.地下水DNAPLs污染多相流的随机模拟及其不确定性分析[J].中国环境科学, 2018,38(7):2572-2579. Wang H, Lu W X, Li J H, et al. Stochastic simulation and uncertainty analysis of multi-phase flow of groundwater polluted by DNAPLs[J]. China Environmental Science, 2018,38(7):2572-2579.
[31]	侯泽宇,卢文喜,王宇.基于替代模型的地下水DNAPLs污染源反演识别[J].中国环境科学, 2019,39(1):188-195. Hou Z Y, Lu W X, Wang Y. Surrogate-based source identification of DNAPLs-contaminated groundwater[J]. China Environmental Science, 2019,39(1):188-195.
[32]	GB/T 14848-2017地下水质量标准[S].