Intelligent enhanced particle filter with deep residual network surrogate for accurate groundwater pollution source characterization

Abstract

Accurate groundwater pollution source characterization (GPSC) is crucial for environmental protection and water resource management. Although the particle filter (PF) algorithm is widely employed for GPSC, it suffers from particle degradation and loss of diversity. To address this challenge, this study proposes an intelligent enhanced particle filter (IEPF) method to enhance GPSC’s accuracy and efficiency. First, this method optimizes the resampling process by defining a weight threshold while introducing an information transmission mechanism. This mechanism enables low-weight particles to update their positions by leveraging information from high-weight particles and neighboring particles, thereby enhancing information exchange among particles and improving the algorithm’s global search capability. Crossover and mutation operations are introduced to further increase the particle diversity, effectively preventing particles from concentrating on local optimal solutions. Furthermore, this study employs a deep residual network (ResNet) as a surrogate model for the simulation model. The ResNet has strong feature learning and expression capabilities and efficiently captures nonlinear relationships in data, thus significantly improving computational efficiency while ensuring accuracy. The results demonstrate that the IEPF method significantly enhances GPSC accuracy. In summary, the proposed IEPF method combined with the ResNet surrogate model provides an efficient and accurate approach to GPSC, with considerable benefits for groundwater pollution management.