Simultaneous Identification of a Contamination Source and Hydraulic Conductivity Based on a Multimodal Direct Forward Machine Learning Model

Groundwater contamination source identification (GCSI) is critical for water resources management but depends on the accurate characterization of aquifer parameters, especially hydraulic conductivity (K). A novel multimodal direct forward machine learning (MDFML) model was developed to simultaneously predict GCSI parameters and reconstruct K-fields. This model utilizes constrained residual fusion to integrate temporal concentration and spatial head data, and improve complementarity. Tested on synthetic Gaussian and non-Gaussian aquifers, MDFML consistently outperformed single-modal models. In Gaussian fields, MDFML improved source parameter prediction by 2.20% (R²) and K-field reconstruction by 7.50% (SSIM, structural similarity index) compared to single-modal baselines. In non-Gaussian fields, structured dispersion patterns achieved higher K-field reconstruction (SSIM=0.951, +6.70% vs. 0.892 for Gaussian), but nonlinearity lowered source prediction accuracy (R²=0.900, -2.75% vs. 0.925 for Gaussian). These results demonstrate the robustness and reliability of MDFML under complex hydrogeological conditions and provide an efficient solution for accurate GCSI and sustainable groundwater remediation.