Simulating California reservoir operation using the classification and regression‐tree algorithm combined with a shuffled cross‐validation scheme

The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstrea...

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Veröffentlicht in:Water resources research Jg. 52; H. 3; S. 1626 - 1651
Hauptverfasser: Yang, Tiantian, Gao, Xiaogang, Sorooshian, Soroosh, Li, Xin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Washington John Wiley & Sons, Inc 01.03.2016
Schlagworte:
Algorithms
altitude
California
Classification
Climate
Climate change
Computer simulation
Control
controlled outflow
Data mining
Data processing
Decision making
decision tree
Decision trees
Drying
Elevation
Forecasting
Hydrologic models
Hydrologic processes
Hydrology
Inflow
Lakes
Management
Mathematical models
Operators
Outflow
Performance prediction
Regression
Regression analysis
Reservoir management
Reservoir operation
Reservoirs
Robustness
Simulation
simulation models
Statistics
Upstream
Water
Water delivery
Water inflow
Water outflow
United States > US
USA, California
California
INE, USA, California
ISSN:0043-1397, 1944-7973
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Beschreibung
Zusammenfassung:The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well‐developed data‐mining models (Classification and Regression Tree) to predict the complicated human‐controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross‐validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others. Key Points: AI&DM approaches are useful tools to assist decision making in reservoir management A newly developed shuffled cross‐validation scheme is robust against overfitting The enhanced CART algorithm is able to reproduce expert reservoir operation decisions
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ISSN:0043-1397
1944-7973
DOI:10.1002/2015WR017394