Investigation into reservoir flood control operations utilizing Decision Tree C5.0

Reservoirs, vital for flood control, require a scientifically sound operational approach to maximize design benefits, ensuring significant flood control advantages and fostering sustainable socio-economic development. Addressing drawbacks in conventional and optimal operations, this study proposes a novel reservoir flood control methodology. The Decision Tree C5.0 algorithm is employed, using Bashan Reservoir as a case study. Initially, the Xinanjiang model for Bashan Reservoir is established using measured flood data, generating simulated floods through rainstorm data. Subsequently, these simulated floods undergo optimal dispatching based on the maximum peak cutting criterion. The resulting flood operation dataset is created from optimal operation schemes, and the Decision Tree C5.0 algorithm extracts the reservoir operation rules decision tree. This decision tree is applied to dispatch four measured floods. Results indicate the feasibility and superiority of operation schemes derived from the decision tree compared to measured schemes. The study introduces the steps and processes of formulating reservoir operation rules using the Decision Tree algorithm in data mining technology, offering decision support for real-time reservoir flood control operations.