Simulating California Reservoir Operation Using the Classification and Regression Tree Algorithm Combined with a Shuffled Cross-Validation Scheme

Abstract:

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 the consideration of policy and regulation, environmental constraints, dry/wet conditions, etc. In this paper, a 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 model’s predictive performance. An application study of 9 major reservoirs in California is carried out and the simulated results from different decision tree approaches are compared with observation, including original CART and Random Forest. The statistical measurements show that CART combined with the shuffled cross-validation scheme gives a better predictive performance over the other two methods, especially in simulating the peak flows. The results for simulated controlled outflow, storage changes and storage trajectories also show that the proposed model is able to consistently and reasonably predict the human’s reservoir operation decisions. In addition, we found that the operation in the Trinity Lake, Oroville Lake and Shasta Lake are greatly influenced by policy and regulation, while low elevation reservoirs are more sensitive to inflow amount than others.