A Machine Learning Approach for Predicting Contaminant Plume Evolution in Groundwater systems

Accurate prediction of the contaminant plumes in groundwater systems are critical for effective pollution management and risk assessment. Effective simulations for reliable predictions require two key pieces of information. The first is detailed knowledge of the aquifer system, including subsurface structures and the hydrogeological heterogeneity of hydraulic parameters. The second is data about the contaminant plume, including its source, spatial distribution, and concentration. However, acquiring and analyzing this data is often costly and labor-intensive due to the extensive collection efforts and complex processing techniques required.

To address these challenges, we developed an innovative machine learning prediction approach. The architecture of the model combines fully connected layers followed by convolutional layers. The training dataset for the machine learning model was generated using a numerical simulation model of groundwater flow and contaminant transport processes in a synthetic aquifer. Monitored contaminant concentration data were used as inputs to the machine-learning model, while contaminant plume distributions (e.g., concentration fields spanning from the initial contaminant release to 10 years in the future) served as outputs. The machine learning models are trained and evaluated under two scenarios: (1) assuming aquifer properties are well-known, (2) aquifer properties are unknown. According to the results, in scenario 1, the prediction of the contaminant field at various time is highly accurate: the predictions resemble the reference at high degree. In scenario 2, prediction accuracy decreased but remained effective: the predicted contaminant plume closely matched the overall structure of the reference distribution. The main advantage of this machine-learning approach is its capability to directly analyze monitoring data and predict the transient groundwater contaminant transport processes, the labor-intensive steps of aquifer characterization and initial contaminant field determination are eliminated. Moreover, the results not only forecast future evolution but also allow for historical tracing, all the way back to its initial release point, thus it provides a comprehensive understanding of the contaminant's lifecycle.