Physics-informed surrogate modeling for supporting climate resilience at groundwater contamination sites

Contamination of soil and groundwater presents a widespread global problem, significantly impacting both human well-being and environmental stability. Conventional models employed for estimating pollutant concentrations under varying climatic conditions demand extensive computational power and high-performance computing resources. In response to this issue, we have devised an innovative method utilizing a physics-informed machine learning technique, known as the U-Net Enhanced Fourier Neural Operator (U-FNO), to generate rapid surrogate models for flow and transport. These models are capable of forecasting groundwater pollution levels under diverse climatic situations and subsurface characteristics without necessitating a supercomputer. In our research, we centered our attention on the Department of Energy's Savannah River Site (SRS) F-Area and established two time-dependent structures: U-FNOB and U-FNOB-R. Both frameworks incorporate a tailored loss function, including specific physical constraints of groundwater flow and transport such as spatial derivatives, and contaminant boundary conditions. The findings of our study indicate that the U-FNO models can consistently foresee spatialtemporal fluctuations in groundwater flow and pollutant transportation properties, such as contaminant concentration, hydraulic head, and Darcy's velocity. Our research reveals that the U-FNOB-R architecture is especially adept at predicting the effects of alterations in recharge rates on groundwater contamination sites, delivering superior time-dependent forecasts compared to the U-FNOB structure. Our novel approach holds the potential to revolutionize environmental monitoring and remediation efforts by providing rapid, precise, and cost-efficient estimations of groundwater pollution levels under uncertain climate conditions.