Drought Forecast Application of BP Prediction Models Based on EMD in Ling River Basin
Yan Yu · Shenyang Nongye Daxue xuebao · 2014
To improve the accuracy of drought prediction, the EMD(Empirical Mode Decomposition) in processing non-stationary single was used to establish BP neural network forecast model. A drought prediction model was established to conduct a drought prediction for precipitation data from 44 stations(11 stations in Xiaoling River Basin, 33 stations in Daling River Basin) in a total of 51 years(1960-2010) in Ling River Basin and compare the forecast results obtaining from BP neural network prediction model and results from EMD of BP neural network. Results showed that the annual average rainfall prediction mean square error(MSE) based on EMD of BP neural network prediction model and BP neural network prediction model in Xiaoling River Basin were 0.0011 and 0.0076, determination coefficient(R2) were 0.95 and 0.83, The annual average rainfall prediction mean square error(MSE) based on EMD of BP neural network prediction model and BP neural network prediction model in Daling River Basin were 0.0032 and 0.0092, determination coefficient(R2) were 0.93 and 0.79, The mean square error(MSE) of BP neural network prediction values based on EMD is smaller, coefficient of determination(R2) is higher and they are all better than the BP neural network and improve the prediction accuracy of BP neural network drought model. Thus, it has a certainly feasibility, could provide the basis for drought control and drought resistance in Ling River Basin and a new method for drought prediction.