Abstract
Machine learning (ML) is increasingly used for predictive analysis in complex environmental systems. However, its effectiveness is often constrained by spatial heterogeneity and resource limitations that restrict data availability. This study evaluates the feasibility and limitations of ML-based predictive modeling for estimating stormwater heavy metal concentrations and associated public health risks in Camden, New Jersey, a historically industrial city with known contamination challenges. In this work, limited stormwater sampling data are integrated with environmental and anthropogenic predictors, including land use, proximity to industries, vegetation, and elevation for machine learning model development. Multiple regression-based ML models, including linear, ridge, lasso, random forest, and support vector regression, are trained and evaluated. In addition, hierarchical modeling approaches are explored to improve predictive performance. Results highlight challenges in predictive generalizability due to spatial heterogeneity and localized contamination hotspots, with models showing limited interpretability across sampling subsets. However, hierarchical modeling approaches incorporating lead concentrations improve predictive performance for arsenic and cadmium. The findings reveal that limited predictive performance can provide valuable diagnostic insight into system complexity, data limitations, and the need for improved sampling strategies. Geographic information system (GIS)-based mapping of model predictions is used to visualize spatial patterns of contamination and identify potential high-risk areas. This work highlights the need for hybrid, interaction-aware modeling frameworks in data-constrained systems to better understand environmental processes. The proposed ML-GIS framework provides a scalable approach for identifying exposure risk patterns and supporting data-informed decision-making in environmental monitoring, urban planning, and public health.