Luo, Shangxue and Zhang, Meiling and Nie, Yamei and Jia, Xiaonan and Cao, Ruihong and Zhu, Meiting and Li, Xiaojuan (2022) Forecasting of monthly precipitation based on ensemble empirical mode decomposition and Bayesian model averaging. Frontiers in Earth Science, 10. ISSN 2296-6463
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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.
Item Type: | Article |
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Subjects: | STM Open Press > Geological Science |
Depositing User: | Unnamed user with email support@stmopenpress.com |
Date Deposited: | 06 Mar 2023 09:05 |
Last Modified: | 23 Sep 2024 04:12 |
URI: | http://journal.submissionpages.com/id/eprint/588 |