Abstract
Scientific prediction of precipitation changes has important guiding value and significance for revealing regional spatial and temporal patterns of precipitation changes, flood climate prediction, etc. Based on the fact that CEEMD can effectively overcome the interference of modal aliasing and white noise, fine composite multi-scale entropy can reorganize the same FCMSE value to reduce the modal component and improve the computational efficiency, and Stacking ensemble learning can effectively and conveniently improve the fitting effect of machine learning, a rainfall prediction method based on CEEMD-fine composite multi-scale entropy and Stacking ensemble learning is constructed, and it is applied to the prediction of monthly precipitation in the Xixia. The results show that, under the same conditions, the CEEMD-FCMSE-Stacking model reduces the root mean square error by 83.48% and 62.08%, and the mean absolute error by 83.25% and 61.84%, respectively, compared with the single Stacking model and CEEMD-LSTM, while the goodness-of-fit coefficients improve by 11.28% and 6.50%, respectively, which means that the CEEMD-FCMSE-Stacking model has higher prediction performance. The CEEMD-FCMSE-Stacking model has higher prediction performance.
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Funding
This work was supported by the Key Scientific Research Project of Colleges and Universities in Henan Province (CN) [grant numbers 17A570004].
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All authors contributed to the study conception and design. writing and editing: Xianqi Zhang and Kai Wang; chart editing: Tao Wang; preliminary data collection: Kai Wang. All authors read and approved the final manuscript.
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Communicated by: H. Babaie
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Zhang, X., Wang, K. & Zheng, Z. A novel integrated learning model for rainfall prediction CEEMD- FCMSE -Stacking. Earth Sci Inform 15, 1995–2005 (2022). https://doi.org/10.1007/s12145-022-00819-2
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DOI: https://doi.org/10.1007/s12145-022-00819-2