Civil Engineering Department, College of Engineering, University of Bisha, Bisha, 61922, Saudi Arabia.
Irrigation and Hydraulics Department, Faculty of Engineering, Tanta University, Tanta, Egypt.
Environ Sci Pollut Res Int. 2021 Apr;28(15):19107-19120. doi: 10.1007/s11356-020-12291-w. Epub 2021 Jan 4.
Management of reservoir systems is a complicated process involving many uncertainties regarding future events and the diversity of purposes these reservoirs serve; therefore, an effective management of these systems could help improve resource utilization and avoid stakeholder disputes. The aim of this paper was to build an optimization-simulation framework based on implicit stochastic optimization (ISO), genetic algorithms (GA), and recurrent neural network (RNN) for addressing the issue of reservoir operation. Inflow scenarios were generated synthetically based on a monthly scale to be used as an input to a multi-objective genetic programming model to construct an optimal operating rules database. Such database was subsequently used simultaneously with the output of the inflow forecasting model to simulate monthly reservoir hedging rules using RNN. Our results demonstrate the effectiveness of the GA-ISO-RNN model for simulating and predicting optimal reservoir release with consistent accuracy. Results from both the training and testing phases clearly proved the usefulness of RNN in predicting optimal reservoir release with relatively higher values of the Nash-Sutcliffe model efficiency coefficient, correlation coefficient, and lower values of root mean squared error and mean absolute deviation. Furthermore, by comparing the historical releases and the output of the proposed model, the results show that the proposed model was less vulnerable than standard operating rules. The proposed methodology was applied to the Bigge reservoir in Germany, as it features an extensive management infrastructure, but this methodology can also be easily adopted in other similar cases.
水库系统管理是一个复杂的过程，涉及到许多未来事件的不确定性和这些水库服务的多样性目的；因此，对这些系统的有效管理可以帮助提高资源利用效率，避免利益相关者的纠纷。本文旨在建立一个基于隐随机优化（ISO）、遗传算法（GA）和递归神经网络（RNN）的优化模拟框架，以解决水库运行问题。根据每月的规模生成了综合的来水情景，作为多目标遗传规划模型的输入，以构建最优运行规则数据库。随后，该数据库与来流预测模型的输出一起，使用 RNN 模拟每月水库套期保值规则。我们的结果表明，GA-ISO-RNN 模型在模拟和预测最优水库放水方面具有一致的准确性。无论是在训练阶段还是测试阶段的结果都清楚地证明了 RNN 在预测最优水库放水方面的有效性，纳什-苏特克里夫模型效率系数、相关系数较高，均方根误差和平均绝对偏差较低。此外，通过比较历史放水和提出的模型的输出，结果表明该模型比标准运行规则更不易受影响。该方法应用于德国的比奇水库，因为它具有广泛的管理基础设施，但该方法也可以很容易地应用于其他类似情况。
