基于投影寻踪和粒子群优化算法的洪水分类研究

Study on Flood Classification Based on Project Pursuit and Particle Swarm Optimization Algorithm

洪水分类实际上是洪水强度大小辨别的优化问题。洪水分类不仅影响着水库的实时调度,而且也影响着洪水灾害危险评估。对利用降水预报进行洪水资源利用的水库来说,洪水分类对水库实时调度规则的建立有着重要的作用。因此,洪水分类是一个重要的理论和实践问题。本文以长江三峡水库代表性水文站——宜昌站为研究对象,基于投影寻踪方法建立了洪水分类的优化模型,并利用粒子群优化算法对所建模型进行求解。结果表明了投影寻踪方法和粒子群优化算法在洪水分类研究中的有效性和合理性。

Flood classification in fact is an optimum problem for recognizing the magnitude of flood intensity. Flood classification will not only affect the real operation of reservoir, bus also influence the risk evaluation of flood disaster. For a reservoir which uses precipitation forecast to carry out floodwater utilization, flood classification plays an important role in proposing the real opera- tion rule under the condition of floodwater utilization. Therefore, flood classification is very important in theory and practice. Tak- ing representative Yichang Hydrometric Station for Three Gorges Reservoir on the Yangtze River as an example, the flood classifica- tion optimum model is supplied based on project pursuit method, and the model is solved by particle swarm optimization algorithm. The results illustrate the validity and reasonability of two combined methods applied to flood classification.