Abstract：Western Jilin was selected as the study area, and the groundwater numerical simulation model for this area was established. Monte-Carlo and Latin Hypercube method were applied to sample exploitation of 10counties (cities) as practicable range in the study area, Latin Hypercube Sampling was selected to obtain input (pumping) and output (water level drawdown) data sets, and wavelet neural network was proposed to establish an surrogate model of the groundwater numerical simulation model, Compared the fitting mean relative error of wavelet neural network model with that of multivariate nonlinear regression model. Two surrogate models both could approach the function of numerical simulation model of groundwater, however, the relative error of mean groundwater level drawdown and the remaining average relative standard deviation of groundwater level drawdown between wavelet neural network model and simulation model was smaller than the multiple nonlinear regression model 76% and 45%, which indicated that the wavelet neural network model can effectively replace groundwater numerical model. This study will provide an effective surrogate method to reduce computational load resulted from multiple invocation of the numerical simulation model of groundwater in the processes of iteration solution by optimization model.
王宇, 卢文喜, 卞建民, 安永凯. 基于小波神经网络的地下水流数值模拟模型的替代模型研究[J]. 中国环境科学, 2015, 35(1): 139-146.
WANG Yu, LU Wen-Xi, BIAN Jian-Min, AN Yong-Kai. Surrogate model of numerical simulation model of groundwater based on Wavelet Neural Network. CHINA ENVIRONMENTAL SCIENCECE, 2015, 35(1): 139-146.
