Application of a Bayesian-Based Integrated Approach for Groundwater Contamination Sources Parameter Identification Considering Observation Error

• Published in: Water (Basel) Vol. 16; no. 11; p. 1618
• Main Authors: Yan, Xueman, An, Yongkai
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Aquifers; Bayesian; Case studies; Contamination; Entropy; Flow velocity; groundwater contamination sources; Groundwater flow; Groundwater pollution; Integrated approach; Markov analysis; Markov chain Monte Carlo; Markov processes; Monte Carlo method; multi-layer perceptron; Neural networks; Parameter identification; Partial differential equations; Probability distribution; relative entropy; Simulation; Simulation methods; Statistical analysis; Water, Underground
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w16111618

Groundwater contamination source (GCS) parameter identification can help with controlling groundwater contamination. It is proverbial that groundwater contamination concentration observation errors have a significant impact on identification results, but few studies have adequately quantified the specific impact of the errors in contamination concentration observations on identification results. For this reason, this study developed a Bayesian-based integrated approach, which integrated Markov chain Monte Carlo (MCMC), relative entropy (RE), Multi-Layer Perceptron (MLP), and the surrogate model, to identify the unknown GCS parameters while quantifying the specific impact of the observation errors on identification results. Firstly, different contamination concentration observation error situations were set for subsequent research. Then, the Bayesian inversion approach based on MCMC was used for GCS parameter identification for different error situations. Finally, RE was applied to quantify the differences in the identification results of each GCS parameter under different error situations. Meanwhile, MLP was utilized to build a surrogate model to replace the original groundwater numerical simulation model in the GCS parameter identification processes of these error situations, which was to reduce the computational time and load. The developed approach was applied to two hypothetical numerical case studies involving homogeneous and heterogeneous cases. The results showed that RE could effectively quantify the differences caused by contamination concentration observation errors, and the changing trends of the RE values for GCS parameters were directly related to their sensitivity. The established MLP surrogate model could significantly reduce the computational load and time for GCS parameter identification. Overall, this study highlights that the developed approach represents a promising solution for GCS parameter identification considering observation errors.