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<p>Existing water quality prediction methods often ignore the true flow direction and flow variations of river networks and employ static topological structures. This leads to distorted depictions of pollutant migration processes, resulting in significant bias in prediction results. This in turn leads to misjudgments in ecological risk assessments, hindering scientific decision-making for targeted pollution control and risk early warning. To address this issue, this paper proposes a Temporal Graph Convolutional Network (T-GCN) model that incorporates river network topological constraints to improve the accuracy of watershed water quality predictions and the reliability of ecological risk assessment. The model constructs a directed graph based on the river network structure and introduces a flow-driven dynamic connection mechanism to adaptively reflect the impact of changing hydrological conditions on pollutant transport paths and velocities. It captures water quality evolution through joint spatiotemporal modeling and embeds hydrophysical constraints to ensure the rationality of prediction results. Experiments show that T-GCN outperforms spatiotemporal graph comparative model such as DCRNN, Graph WaveNet, and AGCRN in predicting four water quality indicators: DO (Dissolved Oxygen), Ammonia Nitrogen (NH<sub>3</sub>–N), Phosphorus (TP), and pH (Potential of Hydrogen). Evaluated in original physical units, T-GCN achieved lower prediction errors on the test set, with MSE of DO, NH<sub>3</sub>–N, TP, and pH being 0.940&#xa0;mg/L, 0.142&#xa0;mg/L, 0.022&#xa0;mg/L, and 0.093, respectively, all outperforming the comparative model. The R<sup>2</sup> for the DO indicator reaches 0.884, and the Kappa coefficient for ecological risk discrimination reaches 0.863, 0.826, and 0.763 at low, medium, and high risk levels, respectively, demonstrating superior temporal and spatial stability. This proposed T-GCN model significantly improves the accuracy of watershed water quality prediction and the reliability of ecological risk assessment, providing a highly reliable prediction tool and decision-making support for smart watershed management and ecological risk prevention and control.</p>

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Spatiotemporal prediction of water quality and ecological risk assessment in a river basin using T-GCN based on river network topology constraints

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月16日

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  • Li Li

摘要

Existing water quality prediction methods often ignore the true flow direction and flow variations of river networks and employ static topological structures. This leads to distorted depictions of pollutant migration processes, resulting in significant bias in prediction results. This in turn leads to misjudgments in ecological risk assessments, hindering scientific decision-making for targeted pollution control and risk early warning. To address this issue, this paper proposes a Temporal Graph Convolutional Network (T-GCN) model that incorporates river network topological constraints to improve the accuracy of watershed water quality predictions and the reliability of ecological risk assessment. The model constructs a directed graph based on the river network structure and introduces a flow-driven dynamic connection mechanism to adaptively reflect the impact of changing hydrological conditions on pollutant transport paths and velocities. It captures water quality evolution through joint spatiotemporal modeling and embeds hydrophysical constraints to ensure the rationality of prediction results. Experiments show that T-GCN outperforms spatiotemporal graph comparative model such as DCRNN, Graph WaveNet, and AGCRN in predicting four water quality indicators: DO (Dissolved Oxygen), Ammonia Nitrogen (NH3–N), Phosphorus (TP), and pH (Potential of Hydrogen). Evaluated in original physical units, T-GCN achieved lower prediction errors on the test set, with MSE of DO, NH3–N, TP, and pH being 0.940 mg/L, 0.142 mg/L, 0.022 mg/L, and 0.093, respectively, all outperforming the comparative model. The R2 for the DO indicator reaches 0.884, and the Kappa coefficient for ecological risk discrimination reaches 0.863, 0.826, and 0.763 at low, medium, and high risk levels, respectively, demonstrating superior temporal and spatial stability. This proposed T-GCN model significantly improves the accuracy of watershed water quality prediction and the reliability of ecological risk assessment, providing a highly reliable prediction tool and decision-making support for smart watershed management and ecological risk prevention and control.