Results for unseen initial RWL for Event No. 1.

Effective reservoir flood control demands real-time decision-making that balances multiple objectives. However, traditional optimization approaches are often too computationally intensive and become intractable when considering dynamically changing preferences of operators, modelled as weights of different objectives. This study aims to develop tractable real-time flood control strategies that maintain performance while reducing computational complexity. We propose two data-driven approaches based on Model Predictive Control (MPC): (1) an explicit MPC using deep neural networks to directly determine optimal outflow schedules, and (2) a switched MPC that produces optimal weights of objectives based on hydrological conditions. Both methods leverage offline learning from an online Parameterized Dynamic MPC framework incorporating state-dependent weights. We tested these approaches on South Korea’s Daecheong multipurpose reservoir using historical flood events with various patterns. The explicit MPC demonstrated reliable performance under conditions similar to its training data. However, it showed frequent changes in outflow schedules and constraint violations for scenarios outside training data. In contrast, the switched MPC maintained robustness across all test scenarios due to a linear optimization process in a receding horizon manner, though with slightly reduced performance compared to the explicit MPC under scenarios inside the range of training data. Most significantly, both approaches reduced computation time from approximately 10 minutes to less than one second, making real-time implementation feasible. This dramatic improvement enables prompt decision-making during rapidly evolving flood events while maintaining near-optimal control performance.

高效水库防洪工作需要兼顾多目标的实时决策。然而，传统优化方法往往计算量庞大，当考虑以不同目标权重形式建模的调度人员动态变化偏好时，将变得难以求解。本研究旨在开发兼具控制性能与低计算复杂度的易处理实时防洪策略。我们提出两种基于模型预测控制（Model Predictive Control，MPC）的数据驱动方法：其一为显式模型预测控制，通过深度神经网络直接确定最优泄流计划；其二为切换型模型预测控制，可基于水文条件生成最优目标权重。两种方法均依托融合状态相关权重的在线参数化动态模型预测控制框架开展离线学习。我们以韩国大清多功能水库为测试对象，采用多类典型历史洪水事件对所提方法进行验证。显式模型预测控制在与其训练数据相似的场景下表现可靠，但在训练数据覆盖范围外的场景中，常出现泄流计划频繁变动与约束违反的问题。与之相对，切换型模型预测控制凭借滚动时域下的线性优化过程，在所有测试场景中均保持了鲁棒性，尽管在训练数据范围内的场景中，其性能略低于显式模型预测控制。最关键的是，两种方法均将计算时长从约10分钟缩短至1秒以内，使得实时部署成为可能。这一显著改进可在快速演进的洪水事件中实现快速决策，同时维持近最优的控制性能。