In this study, a model was proposed based on the sustainable boundary approach, to provide decision support for reservoir ecological operation with the dynamic Bayesian network. The proposed model was developed in four steps: (1) calculating and verifying the sustainable boundaries in combination with the ecological objectives of the study area, (2) generating the learning samples by establishing an optimal operation model and a Monte Carlo simulation model, (3) establishing and training a dynamic Bayesian network by learning the examples and (4) calculating the probability of the economic and ecological targets exceeding the set threshold from time to time with the trained dynamic Bayesian network model. Using the proposed model, the water drawing of the reservoir can be adjusted dynamically according to the probability of the economic and ecological targets exceeding the set threshold during reservoir operation. In this study, the proposed model was applied to the middle reaches of Heihe River, the effect of water supply proportion on the probability of the economic target exceeding the set threshold was analyzed, and the response of the reservoir water storage in each period to the probability of the target exceeding the set threshold was calculated. The results show that the risks can be analyzed with the proposed model. Compared with the existing studies, the proposed model provides guidance for the ecological operation of the reservoir from time to time and technical support for the formulation of reservoir operation chart. Compared with the operation model based on the designed guaranteed rate, the reservoir operation model based on uncertainty reduces the variation range of ecological flow shortage or the overflow rate and the economic loss rate by 5% and 6%, respectively. Thus, it can be seen that the decision support model based on the dynamic Bayesian network can effectively reduce the influence of water inflow and rainfall uncertainties on reservoir operation.

中文翻译：

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基于动态贝叶斯网络的水库生态运行决策支持模型

本研究提出了一种基于可持续边界法的模型，利用动态贝叶斯网络为水库生态运行提供决策支持。提出的模型分为四个步骤：（1）结合研究区生态目标计算和验证可持续边界，（2）通过建立最优运行模型和蒙特卡罗模拟模型生成学习样本，（ 3）通过学习实例建立和训练动态贝叶斯网络；（4）用训练好的动态贝叶斯网络模型计算经济和生态目标不时超过设定阈值的概率。使用建议的模型，可以根据水库运行期间经济和生态目标超过设定阈值的概率动态调整水库的取水量。本研究将提出的模型应用于黑河中游，分析了供水比例对经济目标超过设定阈值概率的影响，以及各时期水库蓄水量对水库蓄水量的响应。计算目标超过设定阈值的概率。结果表明，所提出的模型可以对风险进行分析。与现有研究相比，该模型为水库生态运行时时提供指导，为水库运行图的制定提供技术支持。与基于设计保证率的运行模型相比，基于不确定性的水库运行模型使生态流量不足或溢流率和经济损失率的变化范围分别降低了5%和6%。由此可见，基于动态贝叶斯网络的决策支持模型能够有效降低入水量和降雨不确定性对水库运行的影响。