Application of a GA-BP Neural Network in Accurately Characterizing the Diffusion Range of Groundwater Pollutants

This study addresses the issue of unevenly distributed sampling points, which leads to inaccurate characterization of pollutant diffusion ranges. Using ArcGIS spatial interpolation, the distribution of Mn2+ ions in a chemical park was analyzed, revealing discrepancies due to uneven sampling. To overcome this, two neural network models—GA-BP and standard BP—were applied to predict Mn2+ concentrations at unsampled locations. The GA-BP neural network, optimized with a Genetic Algorithm, showed the best performance, filling gaps in data and allowing for a more accurate concentration distribution map. This revised map was used to delineate the Mn2+ diffusion range, which was further validated with the known production and migration mechanisms of Mn2+. The results demonstrate that the GA-BP model significantly improves the accuracy of pollutant diffusion mapping and offers a more reliable method for environmental pollution assessment, especially in areas with limited sampling data. The BRIEF REPORT is available for this paper at http://www.ykcs.ac.cn/en/article/doi/10.15898/j.ykcs.202409280204.