Comparison of Artificial Neural network (ANN) models and conventional GIS methods to interpolate high-resolution maps of sediment contamination in the Detroit River

Deep learning Artificial Neural Network (DNN) models that included flow velocity, flow directionality, various positional inputs, and sediment total organic carbon (TOC) content as predictor variables were compared to conventional GIS interpolation for predicting sediment contamination of zinc (Zn), mercury (Hg), polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs) in the Detroit River Area of Concern (AOC). A sediment chemistry dataset of 500–850 sampling stations was split into training and validation data across 1000 splits. Universal kriging provided the best interpolation fits relative to other conventional GIS methods including inverse distance weighting and spline fitting. Parsimony-optimized ANN models explained more variation (43.0–58.4%) in validation data splits compared to kriging (19.7– 40.2%) and other GIS approaches. Kriging tended to overestimate contaminant concentrations, especially for areas containing low Hg and PCB levels. ANN performance was improved by adjusting predictions for U.S. restoration areas, non-U.S. restoration areas, and Canadian zones. High-resolution contaminant maps were generated for 9668 cells throughout the AOC. The two approaches showed differences in predicted sediment contamination distribution, with kriging predicting lower river-wide Zn and Hg contamination and higher PCB and PAH contamination. ANNs generated more discrete contaminant hot zones, indicating strong nearshore gradients along the U.S. shoreline and low concentrations in fast moving navigation channels. Given the superior performance of ANNs, this approach was considered a superior interpolation method, with potential applications for guiding future sediment restoration initiatives in the AOC.