Monthly Precipitation Prediction Based on EEMD-SVM-ELM Model
Aiming at the nonlinearity and non-stationary characteristics of surface precipitation data,a support vector regression(SVR)and extreme learning machine(ELM)are constructed as base learners.Firstly,the initial monthly pre-cipitation data is decomposed based on Empirical Mode Decomposition(EEMD).Then the Lempel-Ziv complexity algo-rithm is used to divide the components into high-frequency and low-frequency components.The parameters of the base learner are optimized by particle swarm optimization(PSO).Finally,the EEMD-SVR-ELM monthly precipitation predic-tion model was constructed.Compared with other models,the model has the best comprehensive performance,higher ac-curacy and generalization.Especially compared with the single model,the MMAE,RRMSE,and MMAPE indicators were re-duced by 37.4%,41.4%and 42.5%.The DM test showed that this model was significantly better than other models.This model can be used as an effective new method for monthly precipitation prediction.
NETL
NSTL
万方数据
