Reliable water resources management requires decision support tools to successfully forecast hydraulic data (stage and flow hydrographs). Even though data-driven methods are nowadays trendy to apply, they still fail to provide reliable forecasts during extreme periods due to a lack of training data. Therefore, model-driven forecasting is still needed. However, the model-driven forecasting approach is affected by numerous uncertainties in initial and boundary conditions. To improve the real-time model's operation, it can be regularly updated using measured data in the data assimilation (DA) procedure. Widely used DA techniques are computationally expensive, which reduce their real-time applications. Previous research shows that tailor-made, time-efficient DA methods based on the control theory could be used instead. This paper presents further insights into the control theory-based DA for 1D hydraulic models. This method uses Proportional–Integrative–Derivative (PID) controllers to assimilate computed water levels and observed data. This paper describes the two-stage PID controllers’ tuning procedure. Multi-objective optimization by Nondominated Sorting Genetic Algorithm II (NSGA-II) was used to determine optimal parameters for PID controllers. The proposed tuning procedure is tested on a hydraulic model used as a decision support tool for the transboundary Iron Gate 1 hydropower system on the Danube River, showing that the average discrepancy between modeled and observed water levels can be less than 0.05 m for more than 97% of assimilation window.

可靠的水资源管理需要决策支持工具来成功预测水力数据（阶段和流量过程线）。尽管数据驱动的方法现在很流行，但由于缺乏训练数据，它们仍然无法在极端时期提供可靠的预测。因此，仍然需要模型驱动的预测。然而，模型驱动的预测方法受到初始条件和边界条件中许多不确定性的影响。为了改进实时模型的操作，可以使用数据同化（DA）程序中的测量数据定期更新它。广泛使用的 DA 技术计算量大，降低了它们的实时应用。先前的研究表明，可以改用基于控制理论的量身定制的、省时的 DA 方法。本文进一步介绍了基于控制理论的一维水力模型的 DA。该方法使用比例-积分-微分 (PID) 控制器来同化计算的水位和观测数据。本文描述了两阶段 PID 控制器的整定过程。非支配排序遗传算法II的多目标优化（NSGA-II ) 用于确定 PID 控制器的最佳参数。建议的调整程序在用作多瑙河跨界 Iron Gate 1 水电系统决策支持工具的水力模型上进行了测试，表明建模和观测水位之间的平均差异可以小于 0.05 m，超过 97同化窗口的百分比。