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.
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