The historical data on water intake into the reservoir is collected and used within the framework of a deterministic optimization method to determine the best operating parameters for the dam. The principles that have been used to extract the best values of the flow release from the dam may no longer be accurate in the coming years when the inflow to dams will be changing, and the results will differ greatly from what was predicted. This represents this method’s main drawback. The objective of this study is to provide a framework that can be used to guarantee that the dam is running as efficiently as possible in real time. Because of the way this structure is created, if the dam’s inflows change in the future, the optimization process does not need to be repeated. In this case, deep learning techniques may be used to restore the ideal values of the dam’s outflow in the shortest amount of time. This is achieved by accounting for the environment’s changing conditions. The water evaluation and planning system simulator model and the MOPSO multi-objective algorithm are combined in this study to derive the reservoir’s optimal flow release parameters. The most effective flow discharge will be made feasible as a result. The generalized structure of the group method of data handling (GSGMDH), which is predicated on the results of the MOPSO algorithm, is then used to build a new model. This model determines the downstream needs and ideal release values from the reservoir in real time by accounting for specific reservoir water budget factors, such as inflows and storage changes in the reservoir. Next, a comparison is drawn between this model’s performance and other machine learning techniques, such as ORELM and SAELM, among others. The results indicate that, when compared to the ORELM and SAELM models, the GSGMDH model performs best in the test stage when the RMSE, NRMSE, NASH, and R evaluation indices are taken into account. These indices have values of 1.08, 0.088, 0.969, and 0.972, in that order. It is therefore offered as the best model for figuring out the largest dam rule curve pattern in real time. The structure developed in this study can quickly provide the best operating rules in accordance with the new inflows to the dam by using the GSGMDH model. This is done in a way that makes it possible to manage the system optimally in real time.

收集水库进水的历史数据并在确定性优化方法的框架内使用，以确定大坝的最佳运行参数。在未来几年中，当大坝的流入量发生变化时，用于提取大坝流量最佳值的原理可能不再准确，并且结果将与预测有很大差异。这代表了该方法的主要缺点。本研究的目的是提供一个框架，可用于保证大坝尽可能高效地实时运行。由于这种结构的创建方式，如果未来大坝的流入量发生变化，则不需要重复优化过程。在这种情况下，可以利用深度学习技术在最短时间内将大坝的出流量恢复到理想值。这是通过考虑环境变化条件来实现的。本研究将水评价与规划系统模拟器模型与MOPSO多目标算法相结合，推导水库最优泄流参数。因此，最有效的流量排放将变得可行。然后使用以 MOPSO 算法结果为基础的数据处理组方法 (GSGMDH) 的广义结构来构建新模型。该模型通过考虑特定的水库水预算因素（例如水库的流入量和蓄水量变化）来实时确定下游需求和水库的理想释放值。接下来，将该模型的性能与其他机器学习技术（例如 ORELM 和 SAELM 等）进行比较。结果表明，与ORELM和SAELM模型相比，当考虑RMSE、NRMSE、NASH和R评价指标时，GSGMDH模型在测试阶段表现最好。这些指数的值依次为 1.08、0.088、0.969 和 0.972。因此，它是实时计算最大大坝规则曲线模式的最佳模型。本研究开发的结构可以通过使用 GSGMDH 模型根据大坝的新流入量快速提供最佳运行规则。这样做的方式使得可以实时最佳地管理系统。