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[1]YI Yan-ping,LU Wen-xi,ZHANG Yun,et al.Study on the Surrogate Model of Groundwater Numerical Simulation Model Based on Radial Basis Function Neural Network[J].Research of Soil and Water Conservation,2012,19(04):265-269.

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Study on the Surrogate Model of Groundwater Numerical Simulation Model Based on Radial Basis Function Neural Network

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 19 Number of periods: 2012 04 Page number: 265-269 Column: Public date: 2012-08-20

Title:: Study on the Surrogate Model of Groundwater Numerical Simulation Model Based on Radial Basis Function Neural Network

Author(s):: YI Yan-ping^1,2, LU Wen-xi², ZHANG Yun¹, LU Gui-jun³, WANG Da-zhong³, HONG De-fa⁴; 1. Hongli Soil and Water Conservation Consulting Co., Ltd. of Jilin Province, Changchun 130033, China;
2. College of Environment and Resources, Jilin University, Changchun 130026, China;
3. Jilin Institute of Soil and Water Conservation, Changchun 130033, China;
4. Changchun Institute of Urban Planning&Designing, Changchun 130021, China

Keywords:: surrogate model; radial basis function neural network; Latin Hypercube Sampling; Jinquan Industrial Park

CLC:: P641.8

DOI:: -

Abstract:: In recent years, the surrogate model has become an effective way to connect the numerical simulation model and optimization model. The quality of surrogate model depends on sampling method and the type of surrogate model. In this paper, Jinquan Industrial Park groundwater sources was selected as the study area, the radial basis function neural network model was established as the surrogate model of groundwater numerical simulation model by combining with the groundwater numerical simulation model of the study area, accessing to input (pumping) output (water level drawdown) data set, and using artificial neural network based on Latin Hypercube Sampling techniques. It was proven that the mean water level drawdown output of RBF neural network model and simulation model results fit the average relative error was 0.038; water level drawdown of the remaining average relative standard deviation of the fitting error was 0.042. Fitting the average relative error was small, showing that the radial basis function neural network model can effectively replace groundwater numerical simulation model. The result of research provides a scientific basis for the future in-depth study of surrogate model.

References:: [1] He L,Huang G H,Lu H W.A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design-Part 1:model development[J].Journal of Hazardous Materials,2010,176(1/3):521-526.
[2] Qin X S,Huang G H,He L.Simulation and optimization technologies for petroleum waste management and remediation process control[J].Journal of Environmental Management,2009,90(1):54-76.
[3] 卢文喜.地下水系统的模拟预测和优化管理[M].北京:科学出版社,1999.
[4] Qin X S,Huang G H,Chakma A,et al.Simulation-based process optimization for surfactant-enhanced aquifer remediation at heterogeneous DNAPL-contaminated sites[J].Science of the Total Environment,2007,381(1/3):17-37.
[5] He L,Huang G H,Lu H W,et al.Optimization of surfactant-enhanced aquifer remediation for a laboratory BTEX system under parameter uncertainty[J].Environmental Science & Technology,2008,42(6):2009-2014.
[6] Qin X S,Huang G H,Chakma A,et al.Simulation-based process optimization for surfactant-enhanced aquifer remediation at heterogeneous DNAPL-contaminated sites[J].Science of the Total Environment,2007,381(1/3):17-37.
[7] Johnson V M,Rogers L L.Accuracy of neural network approximators in simulation-optimization[J].Journal of Water Resources Planning and Management (ASCE),2000,126(2):48-56.
[8] Yan S Q,Minsker B.Optimal groundwater remediation design using an adaptive neural network genetic algorithm[J].Water Resources Research,2006,42(5):407-420.
[9] 薛禹群.地下水动力学[M].北京:地质出版社,1997.
[10] McKay M D,Beckman R J,Conover W J.A comparison of three methods for selecting values of input variables in the analysis of output from acomputer code[J].Technometrics,1979,21(2):39-245.
[11] 熊学玉,顾炜.基于改进LHS方法的预应力混凝土结构长期性能概率分析[J].工程力学,2010,27(4):163-168.
[12] 吴振君,王水林,葛修润.LHS方法在边坡可靠度分析中的应用[J].岩土力学,31(4):1047-1054.
[13] Asim R,Sandeep G,Raymond M.A neural-network learning theory and a polynomial time RBF algorithm[J].IEEE Transactionson Neural Network,1997,18(6):1301-1313.
[14] Lin Faajeng,Wai Rongjong,Duan Rouyong.Fuzzy neural networks for identification and control of ultrasonic motor drive with LLCC resonant technique[J].IEEE Transactions on Industrial Electronics,1999,46(5):999-1011.
[15] 王洪斌,杨香兰,王洪瑞.一种改进的RBF神经网络学习算法[J].系统工程与电子技术,2002,24(6):103-105.

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