## 1 Introduction
However, when faced with a sufficiently complex flood control system composed of multiple reservoirs, flood storage and detention areas, lakes, and other infrastructure, conventional optimization algorithms have obvious limitations, including a low convergence efficiency and the \"curse of dimensionality\". For example, as the number of reservoirs increases, the computational scale of DP increases exponentially. To address such issues, modern computing technology has enabled the development of heuristic intelligent algorithms based on artificial intelligence, resulting in general-purpose stochastic search methods that simulate natural selection and biological evolution. As they can be directly applied to address complex problems with nonlinear, discontinuous, non-differentiable, and multi-dimensional characteristics, they have been widely used to optimize flood control operations. At present, the most common heuristic intelligent algorithms include the genetic algorithm [24; 25], non-dominated sorting genetic algorithm [26; 27; 28], particle swarm optimization [29; 30], ant colony optimization [31; 32], artificial neural network [33; 34], support vector machine [35], simulated annealing [36], immune-inspired optimization [37], evolutionary algorithm [38; 39], cultured evolutionary algorithm [40; 41], and honey-bee mating optimization algorithm [42]. However, although heuristic intelligent algorithms can determine an optimal operating policy, there remain many problems in their practical application. The flaws intrinsic to most heuristic intelligent algorithms include premature convergence owing to local fast convergence, poor local search capability owing to a large number of global searches, and long iteration time [43]. Furthermore, the solutions provided by most algorithms are limited by the available calculation time as well as the constraints associated with certain optimizations [44]. At present, both flood control operation methods insufficiently utilize historical reservoir operation data. Importantly, these data contain not only the characteristics of and laws describing runoff generation and inflow from the watershed but also the vast experience of reservoir managers, which provides information supporting operating decisions according to different inflow scenarios. As a considerable body of reservoir operation data has been accumulated by water conservation departments, the use of data mining technology to extract flood control operation rules from these data offers a new method for real-time reservoir flood control operation. The decision tree (DT) algorithm is the most commonly used data mining model, as it creates decision rules and classification results following a tree structure [45]. The DT algorithm has the advantages of being easier to understand, being easier to implement, and requiring relatively less workload than other approaches. Therefore, it has been widely used to address water conservation problems such as flood forecasting [46; 47], flood or drought risk assessment [48; 49; 50; 51; 52; 53], flood or drought classification [54; 55], water quality prediction [56; 57], inter-basin water transfer dispatching [58], water level prediction [59; 60], and hydropower station power generation dispatching [61]. [PERSON] and [PERSON] [46] adopted the boosted decision tree regression to forecast flood water levels in a real-time manner and achieved high forecasting accuracy. [PERSON] et al. [49] confirmed that the tree-based models were more accurate than the stage-damage function from Australia in a flood risk assessment. [PERSON] et al. [54] presented a flood classification for identifying flood patterns at a catchment scale by means of a fuzzy decision tree, and the results showed that this method bore additional potential for analyses of flood patterns. [PERSON] et al. [58] used the decision tree method to determine the diversion amount according to the inter-basin water transfer rules. [PERSON] et al.