Simultaneous identification of groundwater contamination source information, model parameters, and boundary conditions under an unknown boundary mode

Boundary conditions play a crucial role in groundwater contamination source identification (GCSI), but they may be complex and reliable estimates are difficult to obtain in advance in actual situations. If the estimated values deviate significantly from the actual situation, the GCSI results will be inaccurate. However, very few studies have attempted to identify the boundary conditions in GCSI, and even when they are identified, they are often considered too simple. The boundary mode (B_mode) is assumed to be known, but in reality, it is often unknown and is more complex than initially assumed. Previous practices based on this assumption may not accurately reflect actual situations. Therefore, this study focused on the concentration boundaries, and the boundary conditions were also considered unknown variables, along with contamination source information and model parameters. To alleviate the problem of identifying the boundary conditions under an unknown B_mode, we proposed for the first time to treat the B_mode as an unknown variable. Thus, the source information, model parameters, B_mode, and corresponding parameters in the boundary concentration (BC) function were identified simultaneously. The Differential Evolution Adaptive Metropolis with a Snooker Update and Sampling from a Past Archive (DREAM_(ZS)) algorithm and a Kriging surrogate model were used as the primary means of solution. We designed four different synthetic cases to test the effectiveness of the above ideas. When identifying the B_mode, the obtained BC mostly fitted well with the true BC. It was therefore considered feasible for identifying the B_mode. The performance of the DREAM_(ZS) algorithm was found to be superior to the traditional DREAM algorithm and was more efficient.