Abstract
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.
Highlights
With the impact of population growth, urbanization and industrialization, reservoirs play a vital role in regulating water resources by altering the spatial and temporal distribution of natural runoff.The management of reservoirs is often performed by human decision-makers, who are able to combine various hydrologic information, such as past outflows, reservoir water level and forecasted inflows with predefined rules
To rank hydrologic information and judge their value, we try to understand how outflow decisions are made by analyzing historic reservoir operation data based on an outflow simulation model
We prove that forecasted inflow is of great importance to reservoir outflow decisions in the flood season, so forecasted inflow is highly recommended to be included in flood control operating rules
Summary
The management of reservoirs is often performed by human decision-makers, who are able to combine various hydrologic information, such as past outflows, reservoir water level and forecasted inflows with predefined rules. For reservoir operation decision-makers, it is difficult to evaluate which of hydrologic information is the most important. To rank hydrologic information and judge their value, we try to understand how outflow decisions are made by analyzing historic reservoir operation data based on an outflow simulation model. To extract knowledge from data, the attempts of using data-mining techniques for better reservoir operation have gained much popularity in recent years. Bessler et al [1] extracted the operating rules for a reservoir in U.K. using the decision tree algorithm, linear regression and evolutionary algorithm
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