Water level forecasting based on SARIMA-GRU combination model
Compared with traditional single models,combination models have better predictive accuracy under certain condi-tions.To verify whether the combination model is conducive to improving the prediction accuracy of the model,the water lev-el data of Shigui Mountain hydropower station,a tributary of the Yangtze River in the middle reaches are used as the basis.SARIMA model and GRU neural network model are established.The two models are then combined using the inverse vari-ance weighted average method and the IOWA operator.Finally,the predictive accuracy difference between the single model and the combination model is compared with the water level dataset.The results show that appropriate combination methods are conducive to improving model predictive accuracy,and the combination model based on the IOWA operator has excellent predictive performance.
SARIMAGRU neural networkwater level forecastingcombination model
国家科技期刊平台
NSTL
万方数据
