用神经网络模型高效预测雨水设施的水流与污染物动态变化。
Operator-based machine learning framework for generalizable prediction of unsteady treatment dynamics in stormwater infrastructure
- 基于算子学习构建复合神经网络，融合物理规律与数据驱动。
- 水流预测准确率超80%覆盖95.2%测试场景，颗粒物浓度预测72.6%达标。
- 可分析极端低流量下性能下降问题，适合城市排水系统长期评估。
雨水基础设施是分散式城市水管理系统，面临降雨径流带来的剧烈非稳态水力与污染物负荷。精准评估其现场处理性能对经济设计与规划至关重要。传统集总动态模型（如连续搅拌釜反应器，CSTR）计算高效但过度简化输运与反应过程，限制预测精度与洞察力；计算流体动力学（CFD）虽能解析湍流输运与污染物命运的详细物理机制，但对非稳态和长期模拟而言计算成本过高。为解决上述局限，本研究开发了一种复合算子神经网络（CPNN）框架，利用先进的算子学习技术，预测雨水处理中水流与颗粒物（PM）的空间-时间动态。该框架在一种常见城市处理设备——水力分离器（HS）上进行验证。结果表明，CPNN在95.2%的测试案例中实现水流预测的R² > 0.8；颗粒物浓度预测中，72.6%的案例R² > 0.8，22.6%的案例0.4 < R² < 0.8。分析发现，极端低流量条件下的动态捕捉存在挑战，因其对训练损失贡献较小。利用CPNN的自动微分能力，敏感性分析量化了暴雨事件负荷对颗粒物输运的影响。最后，讨论了该框架在实现雨水基础设施连续、长期性能评估方面的潜力，标志着迈向稳健、气候适应型规划与实施的重要一步。
原文摘要 · Abstract (English)
Stormwater infrastructures are decentralized urban water-management systems that face highly unsteady hydraulic and pollutant loadings from episodic rainfall-runoff events. Accurately evaluating their in-situ treatment performance is essential for cost-effective design and planning. Traditional lumped dynamic models (e.g., continuously stirred tank reactor, CSTR) are computationally efficient but oversimplify transport and reaction processes, limiting predictive accuracy and insight. Computational fluid dynamics (CFD) resolves detailed turbulent transport and pollutant fate physics but incurs prohibitive computational cost for unsteady and long-term simulations. To address these limitations, this study develops a composite operator-based neural network (CPNN) framework that leverages state-of-the-art operator learning to predict the spatial and temporal dynamics of hydraulics and particulate matter (PM) in stormwater treatment. The framework is demonstrated on a hydrodynamic separator (HS), a common urban treatment device. Results indicate that the CPNN achieves R2 > 0.8 for hydraulic predictions in 95.2% of test cases; for PM concentration predictions, R2 > 0.8 in 72.6% of cases and 0.4 < R2 < 0.8 in 22.6%. The analysis identifies challenges in capturing dynamics under extreme low-flow conditions, owing to their lower contribution to the training loss. Exploiting the automatic-differentiation capability of the CPNN, sensitivity analyses quantify the influence of storm event loading on PM transport. Finally, the potential of the CPNN framework for continuous, long-term evaluation of stormwater infrastructure performance is discussed, marking a step toward robust, climate-aware planning and implementation.
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