1. Random Simulation of Groundwater Pollution Based on BP Neural Network Substitution Model.
- Author
-
GE Yuan-bo, LU Wen-xi, WANG Zi-bo, WANG Han, and CHANG Zhen-bo
- Subjects
ARTIFICIAL neural networks ,MONTE Carlo method ,GROUNDWATER pollution ,POLLUTION prevention ,RANDOM variables - Abstract
In order to analyze the influence of the uncertainty of hydrogeological parameters on the output of the groundwater numerical simulation model, this paper studies a hypothetical example. First, a groundwater numerical simulation model is established. Then sensitivity analysis is used to screen out the parameters that have a greater impact on the output of the simulation model. As a random variable, in order to reduce the computational load caused by repeatedly calling the simulation model, the Kriging method and the BP neural network method are used to establish the replacement model of the simulation model and the accuracy levels of the two are compared, and the BP neural network replacement model with higher accuracy is selected to perform Monte Carlo simulation. Among them, when the BP neural network method is used to establish the replacement model, the third-point algorithm is used to quickly determine the number of hidden layer nodes that minimize the error of the replacement model. Finally, the results of the random simulation are statistically analyzed and the interval estimation and evaluation of the risk of groundwater pollution are compared. The results show that when the confidence level is 90%, the confidence intervals for the concentration of the three observation wells are 367.48~415.67, 205.12~230.33 and 118.85~132.82 mg/L. Combined with the risk assessment, it is calculated that the risk of groundwater pollution in observation wells No.1, No.2 and No.3 in the study area are 0.66, 0.60, and 0.58, which provides a scientific basis for groundwater pollution prevention and control. [ABSTRACT FROM AUTHOR]
- Published
- 2022