Identification of Groundwater Pollution Sources by a SCE-UA Algorithm-Based Simulation/Optimization Model

Abstract

Prevention and remediation strategies for groundwater pollution can be successfully carried out if the location, concentration, and release history of contaminants can be accurately identified. This, however, presents a challenge due to complex groundwater systems. To address this issue, a simulation-optimization (S/O) model by integrating MODFLOW and MT3DMS into a shuffled complex evolution (SCE-UA) optimization algorithm was proposed; this coupled model can identify the unknown groundwater pollution source characteristics. Moreover, the Grids Traversal algorithm was used for automatically searching all possible combinations of pollution source location. The performance of the proposed S/O model was tested by three hypothetical scenarios with various combinations of mixed situations (i.e., single and multiple pollution source locations, known and unknown pollution source locations, steady-state flow and transient flow). The field measurement errors was additionally considered and analyzed. Our results showed that this proposed S/O model performed reasonably well. The identified locations and concentrations of contaminants fairly matched with the imposed inputs with average normalized deviations less than 1% after sufficient generations. We further assessed the impact of generation number on the performance of the S/O model. The performance could be significantly improved by increasing generation number, which yet resulted in a heavy computational burden. Furthermore, the proposed S/O model performed more efficiently and robustly than the traditionally used artificial neural network (ANN)-based model. This is due to the internal linkage of numerical simulation in the S/O model that promotes the data exchange from external files to programming variables. This new model allows for solving the source-identification problems considering complex conditions, and thus for providing a platform for groundwater pollution prevention and management.

A Dynamic Study of a Karst Spring Based on Wavelet Analysis and the Mann-Kendall Trend Test

“…A multiple regression model was established to examine the average water level during two different periods (1960-1967 and 1968-1989) and precipitation in the current year, urban exploitation, peripheral exploitation and precipitation in the previous year. The following equations were used: Y = 30.417 + 0.002X 1 − 0.278X 2 + 0.002X 3 + 0.0004X 4 (13) Y = 27.362 + 0.002X 1 − 0.002X 2 − 0.05X 3 + 0.001X 4 (14) where X 1 is current precipitation; X 2 is urban exploitation; X 3 is external extraction; and X 4 is precipitation of the previous year. Regression analysis results (Table 2) show that from the 1960s to the 1990s, the main factor affecting groundwater level changed from precipitation to artificial exploitation, and the influence of peripheral mining has exceeded that of urban mining.…”

“…However, the loose rock type water and karst fissure water dynamics are quite different. A karst aqueous medium is highly heterogeneous and anisotropic, and its water circulation process is complex, resulting in karst fissures having a strong water flow [13,14]. This has led to an increase in laboratory-based physical model experiments investigating karst water flow movement [15,16].…”

Identification of source information for sudden hazardous chemical leakage accidents in surface water on the basis of particle swarm optimisation, differential evolution and Metropolis–Hastings sampling

“…The deterministic method based on optimisation is a method to obtain the identification results of pollution sources by minimising the differences between simulated and observed values (Guan et al, 2006;Ayvaz and Tamer, 2016;Huang et al, 2018). For example, Parolin et al (2015) studied the source identification problem of SWPAs using the Luus-Jaakola algorithm, the particle collision algorithm, ant colony optimisation and the golden section method, and the effectiveness of these methods were verified by a case study on the Macae estuary on the southeast coast of Brazil.…”

Identification of source information for sudden hazardous chemical leakage accidents in surface water on the basis of particle swarm optimisation, differential evolution and Metropolis–Hastings sampling

“…This method is used to solve the emergency identification problem, but it sacrifices the precision of the partial solution. The deterministic method based on optimisation is a method to obtain the identification results of pollution sources by minimising the differences between simulated and observed values (Guan et al, 2006;Ayvaz and Tamer, 2016;Huang et al, 2018). For example, Parolin et al (2015) studied the source identification problem of SWPAs using the Luus-Jaakola algorithm, the particle collision algorithm, ant colony optimisation and the golden section method, and the effectiveness of these methods were verified by a case study on the Macae estuary on the southeast coast of Brazil.…”