水库水情预报调度系统关系周边地区人民群众的安全，受到世界各国的重视。系统 的提升迫在眉睫，而智能算法的不断完善使各国学者看到了新的契机，故本文采用改进 的智能算法进行水库水情预报调度系统的研究。 传统的预测调度方法存在很多难以解决的缺点，比如预测算法中假设样本数据无穷 大，且计算复杂度随之不断升高的缺点；比如调度方法中容易陷入局部最优的缺点。本 文基于改进优化的智能算法-最小二乘支持向量机算法和改进粒子群优化算法研究了水 库水情预报调度系统，并分别从算法建模、系统设计和系统实现几个方面进行了系统软 件的设计与实现。系统将数据存储在关系型数据库 Mysql 中，经 Hibernate 层对存储对 象进行持久化后，在 Spring 业务逻辑层实现了基于最小二乘支持向量算法的水情预报与 基于改进粒子群优化算法的水库调度的功能，最后将数据处理结果经由 Struts 2 表示层 进行图形界面结果展示。预报调度系统分为信息管理模块、预报调度模块和系统设置模 块。信息管理模块实现水库基本概况信息、水库特征数据信息以及工情信息的管理；预 报调度模块实现水情预测功能和水库调度功能的实现以及结果的图形化展示；系统设置 模块实现系统中主要参数的设置以及用户信息的设置的实现。 预测调度系统进行测试的结果表明，以最小二乘支持向量机算法和改进粒子群优化 算法为核心的预报调度模型可以精确有效的进行预报与调度洪水，对于充分利用水资源 和防洪防灾具有一定的实用价值。

The reservoir water regime forecasting and dispatching system is related to the safety of the people in the surrounding areas and is valued by all countries in the world. The improvement of the system is imminent, and the continuous improvement of intelligent algorithms has enabled scholars from all over the world to see new opportunities. Therefore, this paper uses improved intelligent algorithms to study the reservoir water regime forecasting and dispatching system. The traditional predictive scheduling method has many shortcomings that are difficult to solve. For example, the prediction algorithm assumes that the sample data is infinite, and the computational complexity is increasing. The scheduling method is easy to fall into the local optimization. This paper studies the reservoir water regime forecasting and dispatching system based on the improved and optimized intelligent algorithm-least squares support vector machine algorithm and improved particle swarm optimization algorithm. The design and implementation of system software are carried out from several aspects of algorithm modeling, system design and system implementation. The system stores the data in the relational database Mysql. After persisting the storage object via the Hibernate layer, In the Spring business logic layer, the water regime prediction based on least squares support vector algorithm and the function of reservoir scheduling based on improved particle swarm optimization algorithm are implemented. Finally, the data processing results are displayed through the Struts 2 presentation layer for graphical interface results. The forecasting and dispatching system is divided into an information management module, a forecasting scheduling module and a system setting module. The information management module realizes the basic general information of the reservoir, the reservoir characteristic data information and the management of the working condition information; the forecasting and dispatching module realizes the realization of the water regime and the realization of the reservoir dispatching function and the graphical display of the results; the system setting module realizes the main parameters of the system. Implementation of setting and setting of user information. After testing the predictive scheduling system through an instance, the results show that the least squares support vector machine algorithm and the improved particle swarm optimization algorithm can predict and dispatch flood more accurately and effectively,and it has certain practical value for making full use of water resources and flood prevention and disaster prevention.