The Value of Hydrologic Information in Reservoir Outflow Decision-Making

• Main Authors: Kebing Chen, Shenglian Guo, Shaokun He, Tao Xu, Yixuan Zhong, Sirui Sun
• Format: Article
• Language: English
• Published: MDPI AG 2018-10-01
• Series: Water
• Subjects: reservoir operations; hydrologic information; data mining; random forests; decision-making; three gorges reservoir
• Online Access: http://www.mdpi.com/2073-4441/10/10/1372

The controlled outflows from a reservoir are highly dependent on the decisions made by the reservoir operators who mainly rely on available hydrologic information, such as past outflows, reservoir water level and forecasted inflows. In this study, Random Forests (RF) algorithm is used to build reservoir outflow simulation model to evaluate the value of hydrologic information. The Three Gorges Reservoir (TGR) in China is selected as a case study. As input variables of the model, the classic hydrologic information is divided into past, current and future information. Several different simulation models are established based on the combinations of these three groups of information. The influences and value of hydrologic information on reservoir outflow decision-making are evaluated from two different perspectives, the one is the simulation result of different models and the other is the importance ranking of the input variables in RF algorithm. Simulation results demonstrate that the proposed model is able to reasonably simulate outflow decisions of TGR. It is shown that past outflow is the most important information and the forecasted inflows are more important in the flood season than in the non-flood season for reservoir operation decision-making.