摘要
应用基于核极限学习机替代模型的模拟-优化理论和方法研究解决了地下水DNAPLs污染源及含水层参数的同步反演识别问题.结果表明:1)核极限学习机替代模型对模拟模型有较高的逼近精度,能够识别并模仿模拟模型的输入-输出关系,绝大部分相对误差小于5%,平均相对误差仅有2.98%;2)以替代模型代替模拟模型,大幅度地减小了模拟-优化过程的计算负荷,将反演识别时间由传统方法的83天减少到3小时,并能够保持较高的计算精度;3)应用基于模拟退火的粒子群优化算法求解优化模型,能够以较快的速度搜寻到全局最优,同时避免搜索过程陷于局部极小解.
Groundwater contamination source identification(GCSI)is critical for taking effective actions in designing remediation strategies,estimating risks,and confirming responsibility.Surrogate-based simulation-optimization technique was applied to source identification and parameter estimation of DNAPLs-contaminated aquifer in this article.The results showed that:1)kernel extreme learning machines(KELM)surrogate model approximated the simulation model accurately.It could simulate the input/output relationship of the simulation model with most of the relative errors less than 5%,and the mean relative error was only 2.98%;2)Replacing the simulation model with a KELM model considerably reduced the computational burden of the simulation-optimization process and maintained high computation accuracy,the identification time was reduced to 3hours from 83days;3)Simulated annealing-based particle swarm optimization algorithm is efficient in searching the global optimal solution of the nonlinear programming optimization model,and avoiding the optimization process trapping into local optimum.
作者
侯泽宇
卢文喜
王宇
HOU Ze-yu;LU Wen-xi;WANG Yu(Key Laboratory of Groundwater Resources and Environment Ministry of Education, Jilin University, Changchun 130021, China;College of Environment and Resources, Jilin University, Changchun 130021, China).)
出处
《中国环境科学》
EI
CAS
CSCD
北大核心
2019年第1期188-195,共8页
China Environmental Science
基金
国家自然科学基金项目(41672232)
吉林省科技发展计划项目(20170101066JC)
关键词
DNAPLs
污染源反演识别
模拟-优化
多相流模拟
核极限学习机替代模型
Dense non-aqueous phase liquids(DNAPLs)
contamination source identification
simulation-optimization
multi-phase flow simulation
KELM surrogate model
