Identification of Groundwater Contamination Source Based on Kalman Filter and Simulation Optimization

This paper studies the problem of identification for groundwater contamination source. Firstly, based on the on-site geological and hydrogeological condition, monitoring data and professional knowledge, a numerical simulation model of groundwater contaminant transport is preliminarily established. The wavelet denoising method is applied to denoise the measured data. Then, the Kalman filtering method is combined with the nonlinear programming optimization model to identify the information of groundwater contamination sources. The Kalman filtering is applied to identify the number and approximate location of contamination sources. On this basis, a nonlinear programming optimization model for groundwater contamination identification is established to identify the exact location and release history of the contamination source. In order to reduce the large amount of computational load generated by repeatedly calling the numerical simulation model in the process of solving the optimization model, Kriging is used to produce a surrogate model for the numerical simulation model, which can be embedded in the nonlinear programming optimization model instead of the numerical simulation model and it can be called directly during the iteration of the optimization model. The research results show that the application of wavelet denoising method can denoise the measured data and make the noisy data closer to the measured value.The Kalman filtering is useful to identify the number and approximate location of the contamination source. Based on the identification of the number and approximate location of the contamination source, the nonlinear programming optimization model can effectively identify the exact location and release history of the contamination source.