|
|
|
| Uncertainty analysis of groundwater pollution simulation model |
| LUO Cheng-ming1,2, LU Wen-xi1,2, WANG Zi-bo1,2, CHANG Zhen-bo1,2 |
1. Key Laboratory of Groundwater Resources and Environmental, Ministry of Education, Jilin University, Changchun 130012, China;
2. College of New Energy and Environment, Jilin University, Changchun 130012, China |
|
|
|
|
Abstract In order to simultaneously analyze the effect of source and sink items and hydrogeological parameters uncertainty on the output of groundwater pollution numerical simulation model, a gangue dump site in Fushun City was taken as an example to study. Firstly, the groundwater pollution numerical simulation model of the site was established with sulfate ion as the simulation factor. Then, local sensitivity analysis and global sensitivity analysis were used to analyze the sensitivity of the simulation model parameters, and the results of thetwowere compared. Finally, two parameters that have a great impact on the model output were selected as the random parameters of the model.In order to reduce the calculation load caused by repeatedly calling the simulation model, four methods of Kriging(KRG), kernel extremelearning machine(KELM), support vector machine(SVM) and BP neural network (BPNN) were used to establish the surrogate model of the simulation model for three observation wells respectively. According to the fitting effect of the four surrogate models in different wells, a surrogate model with the best performance was selected for each well, Monte Carlo stochastic simulation was completed by using the optimized surrogate model. Finally, statistical analysis and risk assessment were carried out on the results of random simulation. The results show that when the confidence was 80%, the confidence intervals of the concentration values of the well 1,2,3 were 211.48~845.04mg/L, 0~406.98mg/L, 231.42~958.37mg/L. In addition, according to the groundwater quality standard and the pollutant concentration distribution function curve of each well, the probability that the water quality of well 1 and well 3 met the class V water standard was 90.1% and 93.1% respectively, and the probability that well 2 met the class III water standard was 80.7%, so as to provide a reasonable basis for groundwater resource management and pollution prevention and control.
|
|
Received: 29 November 2021
|
|
|
|
|
|
| [1] |
王梓博,卢文喜,王涵,等.某钼矿尾矿库地下水污染的随机模拟[J].中国环境科学, 2020,40(5):2124-2131. Wang Z B, Lu W X, Wang H, et al.Stochastic simulation of groundwater pollution in a molybdenum tailings reservoir[J]. China Environmental Science, 2020,40(5):2124-2131.
|
| [2] |
Li F, Zhu J, Deng X, et al. Assessment and Uncertainty Analysis of Groundwater Risk Lifawem[J]. Environmental Research, 2018,160:140-151.
|
| [3] |
常振波,卢文喜,辛欣,等.基于灵敏度分析和替代模型的地下水污染风险评价方法[J].中国环境科学, 2017,37(1):167-173. Chang Z B, Lu W X, Xin X, et al. Groundwater pollution risk assessment method based on sensitivity analysis and surrogate model[J]. China Environmental Science, 2017,37(1):167-173.
|
| [4] |
李久辉,卢文喜,辛欣,等.考虑边界条件不确定性的地下水污染风险分析[J].中国环境科学, 2018,38(6):2167-2174. Li J H, Lu W X, Xin X, et al. Groundwater pollution risk analysis considering uncertainty of boundary conditions[J]. China Environmental Science, 2018,38(6):2167-2174.
|
| [5] |
袁乾,卢文喜,范越,等.基于替代模型的煤矸石堆地下水污染随机模拟[J].中国环境科学, 2019,39(6):2444-2451. Yuan Q, Lu W X, Fan Y, et al. Stochastic simulation of groundwater pollution in gangue dump based on surrogate model[J]. China Environmental Science, 2019,39(6):2444-2451.
|
| [6] |
高鑫宇,曾献奎,吴吉春.基于改进稀疏网格替代模拟的地下水DNAPLs运移不确定性分析[J].水文地质工程地质, 2020,47(1):1-10. Gao X Y, Zeng X K, Wu J C. Uncertainty analysis of groundwater DNAPLs migration based on improved sparse grid instead of simulation[J]. Hydrogeology Engineering Geology, 2020,47(1):1-10.
|
| [7] |
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. An surrogate model for simulation of multiphase flow for groundwater DNA PLs contamination remediation[J]. China Environmental Science, 2019,39(7):2913-2920.
|
| [8] |
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, et al. Stochastic simulation of groundwater pollution based on BP neural network surrogate model[J]. Rural Water Resources and Hydropower in China, 2022,(3):107-113,119.
|
| [9] |
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. Stochasticsimulation of multiphase flow contaminated by groundwater DNAPLs and analysis of uncertainty[J]. China Environmental Science, 2018,38(7):2572-2579.
|
| [10] |
Luo J, Lu W. Sobol'sensitivity analysis of NAPL-contaminated aquifer remediation process based on multiple surrogates[J]. Computers&Geosciences, 2014,67:110-116.
|
| [11] |
SOBOL'I M. Sensitivity estimates for nonlinear mathematicalmodels[J]. Mathematical Modelling Computer Experiments, 1993,1(4):407-414.
|
| [12] |
王浩昌,杜鹏飞,赵冬泉,等.城市降雨径流模型参数全局灵敏度分析[J].中国环境科学, 2008,(8):725-729. Wang H C, Du P F, Zhao D Q, et al. Global sensitivity analysis of urban rainfall-runoff model parameters[J]. China Environmental Science, 2008,(8):725-729.
|
| [13] |
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,(5):491-495. Shu L C, Wang M M, Liu R G, et al. Sensitivity analysis of parameters in groundwater numerical simulation[J]. Journal of Hohai University (Natural Science Edition), 2007,(5):491-495.
|
| [14] |
刘波,金爱兵,高永涛,等.基于Morris法的单裂隙岩体温度场参数灵敏度分析[J].采矿与安全工程学报, 2016,33(1):152-157. Liu B, Jin A B, Gao Y T, et al. Sensitivity analysis of temperature field parameters of single fractured rock mass based on Morris method[J]. Journal of Mining and Safety Engineering, 2016,33(1):152-157.
|
| [15] |
贾顺卿,卢文喜,李久辉,等.基于U-D分解卡尔曼滤波地下水污染源溯源辨识[J].中国环境科学, 2021,41(2):713-719. Jia S Q, Lu W X, Li J H, et al. Inverseidentification of groundwater pollution source based on U-D decomposition Kalman filter[J]. China Environmental Science, 2021,41(2):713-719.
|
| [16] |
李萍,曾令可,税安泽,等.基于MATLAB的BP神经网络预测系统的设计[J].计算机应用与软件, 2008,(4):149-150,184. Li P, Zeng L K, Shui A Z, et al. Design of BP neural network prediction system based on MATLAB[J]. Computer Applications and Software, 2008,(4):149-150,184.
|
| [17] |
潘紫东,卢文喜,范越,等.基于模拟-优化方法的地下水污染源溯源辨识[J].中国环境科学, 2020,40(4):1698-1705. Pan Z D, Lu W X, Fan Y, et al. Inverseidentification of groundwater pollution sources based on simulation-optimization method[J]. China Environmental Science, 2020,40(4):1698-1705.
|
| [18] |
Wu S, Wang Y, Cheng S. Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system[J]. Neurocomputing, 2013,102(FEB.15):163-175.
|
| [19] |
GB/T 14848-2017地下水质量标准[S]. GB/T 14848-2017 Groundwater quality standard[S].
|
|
|
|
