Research on Monthly Runoff Prediction Based on NRBO-SVM Model
Based on the multi-year monthly runoff data from the Yeller Station,the methods to improve the accuracy of monthly runoff prediction are explored in terms of model inputs,model optimization and outputs,using the support vector machine(SVM)as a predictor.Firstly,the performance of Newton-Raphson optimization algorithm(NRBO)and Gray Wolf Optimization(GWO)algorithm in parameter optimization is compared,and it is found that NRBO performs better when the mean square error(MSE)is used as the fitness function.Further comparing the efficacy of time series forecasting with split-month forecasting,the results show that time series forecasting has higher forecasting accuracy.In addition,based on above forecasting results,this study also explores the effect of combining the forecasting outputs,which is found to be effective in improving the generalization performance of the model.In the data preprocessing session,preprocessing by variational modal decomposition(VMD)can significantly reduce the difficulty of model prediction while significantly improving the prediction accuracy.Specifically,GWO-VMD-NRBO-SVM reduces the mean absolute percentage error(MAPE)and normalized root-mean-square error(NRMSE)by more than 68%and 79%,respectively,and improves the Nash efficiency coefficient(NSE)by more than 15%compared to a single model.The results of this paper are informative for non-stationary monthly runoff prediction.
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