水力发电学报2017，Vol.36Issue(8)：34-42,9.DOI:10.11660/slfdxb.20170804
融合大气环流异常因子的径流预报研究
Runoff prediction incorporating anomalous atmospheric circulation factors
摘要
Abstract
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 (GASVR) 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.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Key words
runoff prediction/genetic algorithm/support vector regression/prediction model/meteorological factor分类
天文与地球科学引用本文复制引用
孟二浩,黄生志,黄强,刘登峰,白涛..融合大气环流异常因子的径流预报研究[J].水力发电学报,2017,36(8):34-42,9.基金项目
陕西省水利科技计划项目(2017slkj-19) （2017slkj-19）
国家自然科学基金(91325201) （91325201）
水利部公益项目(201501058) （201501058）
陕西省水利科技计划项目(2016slkj-8) （2016slkj-8）
