A graph-based machine learning framework for river water quality management under data limitations

Accurate prediction of riverine water quality is often hindered by sparse sampling and limited streamflow data, a common outcome of resource-constrained watershed monitoring. To address this, we propose a three-module machine-learning framework-prediction (graph neural networks or recurrent networks), interpretation (explainable AI), and management (counterfactual analysis)-and apply it to chromaticity prediction in the Hantan River Basin, Republic of Korea. The dataset includes 1667 monthly observations from 59 monitoring sites (December 2021-October 2024) covering 37 hydro-environmental variables. Performance was assessed using independent training, validation, and test sets. Graph-based models outperformed the recurrent baseline, with the enhanced Graph Sample-and-Aggregate model achieving a test R² of 0.82, demonstrating that representing pollution-source characteristics and transport pathways improves prediction. Interpretability analyses revealed management-relevant insights: PGExplainer highlighted strong upstream influences from the SC sub-watershed, identifying it as the primary intervention region. Feature attribution distinguished long-term influences (e.g., TOC near major WWTPs) from short-term episodic drivers associated with facility-specific effluent spikes. Counterfactual analyses quantified the reductions in effluent chromaticity and proximal indicators required to achieve downstream targets at site HT Y4. Counterfactual success rates-defined as the proportion of model-generated cases meeting the target-were 26 % and 40 % for chromaticity targets of 14 and 15 color units (CU), respectively. Given these outcomes and considering that 14-15 CU is generally acceptable for basin-scale management, a downstream target of 14-15 CU was proposed as feasible and practical. Overall, the framework serves as a cost-effective and interpretable decision-support tool for watershed management under data-limited monitoring conditions.