Characterizing multi-source heavy metal contaminated sites at the Hun River basin: An integrated deep learning and data assimilation approach

Abstract

In real-world scenarios involving groundwater contamination, the environmental complexity substantially complicates the tasks of tracing pollution sources and characterizing the features of affected sites. To address these challenges, this study presents an integrated framework that combines deep learning (AR-Net-DA) with data assimilation (ES-MDA). This approach effectively traces pollution sources and characterizes site features using sparse data from complex contamination scenarios. The paper introduces a case study involving multisource heavy metal (manganese) pollution in the Hun River basin, Liaoning Province, China. A high-fidelity model for groundwater flow and solute transport was developed. Subsequently, the innovative convolutional neural network model, AR-Net-DA, was employed to replace traditional process-based groundwater models by dynamically optimizing weights in proximity to various pollution sources. This model was then integrated into the ES-MDA inversion framework to concurrently determine pollution source parameters and the spatial distribution of aquifer permeability fields. The results demonstrate that this coupled inversion framework can accurately pinpoint pollution source locations and their release histories using limited observational data, while also mapping the spatial distribution of hydraulic conductivity fields with enhanced computational efficiency. These findings have significant implications for groundwater resource management, pollution risk control, and the remediation of contaminated sites.