A Variety Of Combination Forecasting Method And Comparing Of Medium And Long-term Stream-flow

The problems of shortage in water resources and ecological environment deteriorationhas become a key factor in restricting the sustainable development of northwest inland rivereconomic in China. With the continuous development of the socio-economic, the requirementof various departments of the national economy on the hydrological forecasting is gettinghigher and higher, not only requires short-term forecasts of higher accuracy, but requireslong-term stream-flow forecasting. Therefore, long-term stream-flow forecasting which isbased on the combination model is of great significance to the effective regulation of the riverbasin water resources and rational use of water resources. This paper based on the applicationof individual prediction methods, applied various combination forecasting methods ofstream-flow forecasting to predict the Shiyang River Basin tributaries drought periodstream-flow and the annual stream-flow, The main research contents and the main conclusionsobtained as follows:(1) Summarized the currently main methods of stream-flow forecasting, including fourindividual prediction methods: multiple linear regression (MLR), the BP neural networks,support vector machine (SVM) and autoregressive moving average (ARIMA). In order toimprove the accuracy of the forecasts of annual stream-flow, introduct eight combinationsforecasting techniques: the simple average method (SA), the weighted average method (WA)regression methods (Regression), the sum of squared errors method (SSE), artificial neuralnetwork (ANN) and time-varying weights method (including the time-varying sum of squarederror method (TSSE), linear time-varying sum of squared error (LTSSE) and geometrictime-varying sum of squared error (GTSSE)). We can obtained more accurate predictionresults through constitute the error information of single forecasting model.(2) Apply three individual prediction methods which is including Mmultiple linearregression (MLR), BP neural network and autoregressive moving average(ARIMA)forecasted the drought period stream-flow of Xiying River of the Shiyang River,Test the fitting accuracy of the models with the relative root mean square error (R-RMSE)and the relative bias (R-Bias), the results showed that the ARIMA method has the highestaccuracy among the single models; The accuracy of the weighted average combination is higher than the simple average combination, and fitting accuracy of the ARIMA-MLRcombination and ARIMA-BP combination are better.(3) Apply four individual prediction methods which is including multiple linearregression (MLR), the BP neural network, support vector machine (SVM) and theautoregressive moving average (ARIMA) to forecast annual stream-flow from eight landscaperiver of the Shiyang River Basin according to the pre-meteorological data. Test the fittingaccuracy of the models with R-RMSE and R-Bias, the results showed that the SVM methodhas the highest accuracy.(4) Apply eight combination methods, combinating four individual prediction models ofannual stream-flow, this reached a total of11possible of combinations, and compare theaccuracy of models in relative root mean square error(R-RMSE) and the relative bias(R-Bias)of the prediction results. In theory:①When the forecasting error variance ratio of SA andWA methods, and when a prediction is relative poorer than another, WA method performs wellthan SA method. Although SA does not always perform better than that of the best constituentforecast, it was always much better than that of the worst constituent forecasts.②When theerror of individual forecasting is non-stability, time-varying weights combination methodcould produce better results than the constant weight method.③One cannot expectcombining technique to yield significant improvement when two constituent forecasts arehighly correlated. In such cases, using the best constituent forecast model is recommended. Atthis point, the ANN combining method, which has a more flexible structure and can moreaccurately represent complex and nonlinear relationships between constituent forecasts thanthe linear combining approaches, its R-RMSE is lower than other combination.④Thecombining methods based on regression and ANN can remove the effects of bias in theconstituent forecasts. By applying a technique with a bias correction component, one canimprove the combined forecast over using other techniques without a bias correctioncomponent.(5) Through compare the R-RMSE of various combinations, obtained the optimalcombinations of Shiyang River eight rivers stream-flow forecasting. The optimal combinationof Dajing River is M*SA combination in ANN methods; The optimal combination of GulangRiver is M*SA combination in LTSSE and GTSSE methods; The optimal combination ofHuangyang River is MB*SA combination in ANN methods; The optimal combination ofZamu River is M*BSA combination in Regression methods; The optimal combination of JintaRiver is MB*SA combination in Regression and ANN methods; The optimal combination ofXiying River is M*SA combination in TSSE,LTSSE and GTSSE methods; The optimalcombination of Dongda River is MB*S combination in WA,Regression and ANN methods; The optimal combination of Xida River is M*BSA combination in WA,Regression and ANNmethods; On the whole: WA,Regression and ANN combination methods perform best, Whenthe error is non-stability, TSSE,LTSSE and GTSSE combination methods can improve theaccuracy; and the accuracy of M*BSA combination,MB*SAcombination and SA combinationare the highest.