Spatial-temporal analysis of groundwater well features from neural network prediction of hexavalent chromium concentration

Groundwater monitoring is a crucial part of groundwater remediation that produces data from various strategically placed wells to maintain a water quality standard. Using the United States Department of Energy's Hanford 100-HRD area well data, recurrent neural networks are trained in the form of one-dimensional Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, and Dual-stage Attention-based LSTM (DA-LSTM) networks to reduce monitoring costs and increase data sampling responsiveness that is subject to laboratory analysis delays, with the best network being DA-LSTM achieving an R² score of 0.82. There is also the provision of novel well relation analysis through two method of explanation coming from SHapley Additive exPlanations (SHAP) and attention mechanisms within DA-LSTM. Extracting feature well importance from these two methods and using target wells as vehicles for pipeline demonstration, it is found that wells 199-D5-160, 199-D4-68, 199-D2-11, 199-D8-94, and 199-D2-10 have had the most year-long impact on Hexavalent Chromium (CrVI) concentration at the target during the testing year, while wells 199-D8-94, 199-D4-95, 199-D8-53, 199-D4-85, and 199-D4-99 are found to be strong candidates affecting target CrVI instantaneous change during a particular timeframe.