Identification of light nonaqueous phase liquid groundwater contamination source based on empirical mode decomposition and deep learning

Abstract

The simulation optimization method was used to the identification of light nonaqueous phase liquid (LNAPL) groundwater contamination source (GCS) with the help of a hypothetical case in this study. When applying the simulation optimization method to identify GCS, it was a common technical means to establish surrogate model for the simulation model to participate in the iterative calculation to reduce the calculation load and calculation time. However, it was difficult for a single modeling method to establish surrogate model with high accuracy for the LNAPL contamination multiphase flow simulation model (MFSM). To give full play to advantages of single surrogate model and improve the accuracy of the surrogate model to the MFSM, a combination of deep belief neural network (DBNN) and long short-term memory (LSTM) neural network was used to establish artificial intelligence ensemble surrogate model (AIESM) for the MFSM. At the same time, to reduce the influence of noise in observed concentrations on the accuracy of the identification results, empirical mode decomposition (EMD) and wavelet analysis methods were used to denoise the observed concentrations, and their noise reduction effects were compared. The observed concentrations with better noise reduction effect and the observed concentrations without denoising were used to construct the objective function, and constraints of the optimization model were determined meanwhile. Then, the objective function and the constraints were integrated to build the optimization model to identify GCS and simulation model parameters. Applying the AIESM instead of the MFSM to embed in the optimization model and participate in the iterative calculation. Finally, the genetic algorithm (GA) was used to solve the optimization model to obtain the identification results of GCS and simulation model parameters. The results showed that compared with the single DBNN and LSTM surrogate models, AIESM obtained the highest accuracy and could replace the MFSM to participate in the iterative calculation, thereby reducing the calculation load and calculation time by more than 99%. Comparing with the wavelet analysis, EMD could reduce the noise in the concentrations more effectively, improved the accuracy of the approximated concentrations to the actual values, and increased the accuracy of the GCSs identification results by 1.45%.

Keywords: AIESM; DBNN; Empirical mode decomposition; LNAPL contamination; LSTM; Noise reduction; Source identification.