A Novel Simulation-Optimization Model Built by FloPy: Pollutant Traceability in a Chemical Park in China

Abstract

Heavy metal pollution of groundwater will not only destroy the ecological environment but also negatively affect the functioning of the human liver. Tracing the source of groundwater pollution is an important way to protect groundwater resources. FloPy is promoting the use of big data in the groundwater field, especially in groundwater resource planning and management and contaminant traceability. This paper takes Mn as an example and codes a simulation-optimization model for solving the groundwater pollutant traceability problem using FloPy. The Bayesian optimization and strengthen elitist genetic algorithm (SEGA) algorithms are then used to optimize the hydraulic conductivity and pollutant sources in the study area. The results show that the model runs in 411 s, which is an acceptable amount of time spent, the slope of the fitted curve between the model-calculated water level and the actual observed water level is 0.914, and the contaminant traceability results can successfully locate the contaminant sources in real engineering problems. The numerical groundwater flow model and solute transport model can be quickly built, modified, and run by writing code, and can be easily and efficiently coupled with various optimization algorithms with FloPy.