摘要
径流预报对区域水资源开发与管理具有重要的作用,当前的研究主要聚焦在先进的算法而忽视了丰富预报因子对提高径流预报精度的贡献。本研究以泾河径流为例,将遗传算法(GA)和回归支持向量机模型耦合,建立了改进的支持向量机回归模型(GA-SVR)。预报变量在常规预报因子(降雨与蒸发)的基础上增加了对径流影响较强的大气环流异常因子。结果表明,预测变量未含大气环流异常因子的情况下,GA-SVR模型的预测精度和泛化能力皆优于神经网络模型(ANN);考虑大气环流异常因子后,GA-SVR模型预测精度进一步提高。由此说明,SVR模型耦合GA后可提高月径流的预报精度,考虑大气环流异常因子后其预测精度可进一步提高。
Runoffprediction plays a key role in development and management of regional water resources Previous research focused largely on advanced algorithms, often ignoring the contribution of expanding predictors to runoffprediction. This study has developed an improved support vector machine model （GA- SVR） coupling the genetic algorithm （GA） and a support vector regression （SVR） model through a case study of predicting the runoff in the Jing River. In addition to the conventional forecasting factors （i.e. rainfall and evaporation）, predictor variables in this model also cover anomalous atmospheric circulation factors that have a strong influence on the runoff. Results indicate that the GA-SVR model achieves a prediction accuracy and generalization ability significantly better than the neural network model （ANN） when not using these anomalous factors and when putting them into use, the accuracy is further improved. Thus, accuracy in monthly runoff prediction can be improved by coupling a SVR model with GA and further improved by considering anomalous atmospheric circulation factors.
出处
《水力发电学报》
EI
CSCD
北大核心
2017年第8期34-42,共9页
Journal of Hydroelectric Engineering
基金
陕西省水利科技计划项目(2017slkj-19)
陕西省水利科技计划项目(2016slkj-8)
国家自然科学基金(91325201)
水利部公益项目(201501058)
关键词
径流预报
遗传算法
回归支持向量机
预测模型
大气环流异常因子
runoff prediction
genetic algorithm
support vector regression
prediction model
meteorological factor
