@ARTICLE{10.3389/feart.2022.926067, AUTHOR={Luo, Shangxue and Zhang, Meiling and Nie, Yamei and Jia, Xiaonan and Cao, Ruihong and Zhu, Meiting and Li, Xiaojuan}, TITLE={Forecasting of monthly precipitation based on ensemble empirical mode decomposition and Bayesian model averaging}, JOURNAL={Frontiers in Earth Science}, VOLUME={10}, YEAR={2022}, URL={https://www.frontiersin.org/articles/10.3389/feart.2022.926067}, DOI={10.3389/feart.2022.926067}, ISSN={2296-6463}, ABSTRACT={Precipitation prediction is crucial for water resources management and agricultural production. We deployed a hybrid model based on ensemble empirical mode decomposition (EEMD) and Bayesian model averaging (BMA), called EEMD-BMA, for monthly precipitation series data at Kunming station from January 1951 to December 2020. Firstly, the monthly precipitation data series was decomposed into multiple Intrinsic Mode Functions (IMFs) and a residue with EEMD. Next, autoregressive integrated moving average (ARIMA), support vector regression (SVR) and long short-term memory (LSTM) models are used to predict components respectively. The prediction results of EEMD-ARIMA, EEMD-SVR and EEMD-LSTM are obtained by summing the prediction results of each component. Finally, BMA is used to combine the prediction results of the EEMD-ARIMA, EEMA-SVR and EEMD-LSTM models, whose weights are calculated by birth-death Markov Chain Monte Carlo algorithm. The results show that the proposed EEMD-BMA model provides more accurate precipitation predictions than the individual models; the RMSE is 17.2811 mm, the MAE is 12.6999 mm and the R2 is 0.9573. Moreover, the coverage probability (CP) and mean width (MW) of the 90% confidence interval for the predicted values of the EEMD-BMA model are 0.9375 and 60.315 mm, respectively. Therefore, the proposed EEMD-BMA model has good application prospects and can provide a basis for decision makers to develop measures against potential disasters.} }