Abstract
Accurate forecasting of monthly precipitation is of great significance for national production, disaster prevention and mitigation, and water resources allocation management. However, it is difficult for individual models to accurately accomplish the task of predicting precipitation, and there is also the problem of insufficient accuracy of peak and trough prediction. Therefore, to solve this problem, this paper will provide a CEEMDAN-SVM-LSTM model that combines a fully adaptive noise ensemble empirical modal decomposition (CEEMDAN),a support vector machine (SVM) and the long short-term memory (LSTM) neural network. The CEEMDAN algorithm is first used to decompose the precipitation time series data into different modal components, the SVM model is then applied to the first modal component and the LSTM is applied to the remaining modal components. The precipitation data of Lanzhou city is taken as an example and brought into the model for testing and comparing with the performance of single LSTM model, differential integrated moving average autoregressive model (ARIMA), back propagation (BP) neural network model,support vector machine(SVM), extreme gradient boosting(XGBOOST), CEEMDAN-LSTM model and CEEMDAN-SVM model. After the experimental verification, the CEEMDAN-SVM-LSTM model effectively improves the fit between the observed and predicted values, overcomes the problem of low accuracy of peak and trough prediction, and significantly outperforms other models.
Similar content being viewed by others
Data availability
Not applicable.
References
Cao J, Li J, Yin M et al (2022) Online reviews sentiment analysis and product feature improvement with deep learning[J]. ACM Trans Asian Low-Resour Lang Inf Process. https://doi.org/10.1145/3522575
Chen Y, Huang J, Sheng S et al (2018) A new downscaling-integration framework for high-resolution monthly precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data[J]. Remote Sens Environ 214:154–172
Dai H, Huang G, Zeng H, Yu R (2022) Haze risk assessment based on improved PCA-MEE and ISPO-LightGBM model. Syst 10(6):263
Das J, Jha S, Goyal MK (2020) On the relationship of climatic and monsoon teleconnections with monthly precipitation over meteorologically homogenous regions in India: Wavelet & global coherence approaches[J]. Atmos Res 238:104889
Ehteram M, Sammen SS, Panahi F et al (2021) A hybrid novel SVM model for predicting CO2 emissions using Multiobjective Seagull Optimization[J]. Environ Sci Pollut Res 28(46):66171–66192
Ghaemi E, Tabesh M, Nazif S (2022) Improving the ARIMA Model Prediction for Water Quality Parameters of Urban Water Distribution Networks (Case Study: CANARY Dataset)[J]. Int J Environ Res 16(6):1–10
Giang NH, Wang YR, Hieu TD et al (2022) Monthly precipitation prediction using neural network algorithms in the Thua Thien Hue Province[J]. J Water Clim Chang 13(5):2011–2033
Huang, Z, Xu, W, Yu, K (2015) Bidirectional LSTM-CRF models for sequence tagging. arXiv 2015[J]. arXiv preprint arXiv:1508.01991
Kumar D, Singh A, Samui P et al (2019) Forecasting monthly precipitation using sequential modelling[J]. Hydrol Sci J 64(6):690–700
Li M, Liu M, Liu X et al (2022) Decomposition of long time-series fraction of absorbed photosynthetically active radiation signal for distinguishing heavy metal stress in rice[J]. Comput Electron Agric 198:107111
Liao PC, Pan Y (2021) Kindergarten space design based on BP (back propagation) neural network. J Korea Converg Soc 12:1–10
Mendez M, Maathuis B, Hein-Griggs D et al (2020) Performance evaluation of bias correction methods for climate change monthly precipitation projections over Costa Rica[J]. Water 12(2):482
NajwaMohd Rizal N, Hayder G, Mnzool M et al (2022) Comparison between Regression Models, Support Vector Machine (SVM), and Artificial Neural Network (ANN) in River Water Quality Prediction[J]. Processes 10(8):1652
Qasim M, Amin M, Omer T (2020) Performance of some new Liu parameters for the linear regression model[J]. Commun Stat-Theory Methods 49(17):4178–4196
Rongbin C, Sanming L (2021) Research on wind power prediction method based on CEEMDAN-SSA-GRU[C]//2021 IEEE International Conference on Electrical Engineering and Mechatronics Technology (ICEEMT). IEEE 2021:597–601
Run L, Min LX, Lu ZX (2020) Research and comparison of ARIMA and grey prediction models for subway traffic forecasting[C]//2020 International Conference on Intelligent Computing, Automation and Systems (ICICAS). IEEE 2020:63–67
Siddique R, Mejia A, Brown J et al (2015) Verification of precipitation forecasts from two numerical weather prediction models in the Middle Atlantic Region of the USA: A precursory analysis to hydrologic forecasting[J]. J Hydrol 529:1390–1406
Song C (2022) An evaluation method of english teaching ability based on deep learning[J]. Security and Communication Networks 2022. https://doi.org/10.1155/2022/8339137
Tian Z, Wang J (2022) A novel wind speed interval prediction system based on neural network and multi-objective grasshopper optimization[J]. International Transactions on Electrical Energy Systems 2022. https://doi.org/10.1155/2022/5823656
Xie Y, Zhao J, Qiang B et al (2021) Attention mechanism-based CNN-LSTM model for wind turbine fault prediction using SSN ontology annotation[J]. Wirel Commun Mob Comput 2021:1–12
Yang H, Li X, Qiang W et al (2021) A network traffic forecasting method based on SA optimized ARIMA–BP neural network[J]. Comput Netw 193:108102
Yang K, Bi M, Liu Y et al (2019) LSTM-based deep learning model for civil aircraft position and attitude prediction approach[C]//2019 Chinese control conference (CCC). IEEE 2019:8689–8694
Yun P, Huang X, Wu Y et al (2022) Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM[J]. Energy Sci Eng 11:79–96
Zeng H, Shao B, Dai H, Yan Y, Tian N (2023) Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM. Energy 263:126125
Zhou Y, Li T, Shi J, Qian Z (2019) A CEEMDAN and XGBOOST-based approach to forecast crude oil prices. Complex 2019:4392785
Zou P, Hou B, Lei J et al (2020) Bearing fault diagnosis method based on EEMD and LSTM[J]. Int J Comput Commun Control 15(1).
Funding
This research received no external funding.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Wenchao Ban and Ziyi Shen. The first draft of the manuscript was written by Ziyi Shen. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Conflict of interest
The authors declare no conflicts of interest.
Additional information
Communicated by H. Babaie.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Shen, Zy., Ban, Wc. Machine learning model combined with CEEMDAN algorithm for monthly precipitation prediction. Earth Sci Inform 16, 1821–1833 (2023). https://doi.org/10.1007/s12145-023-01011-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-023-01011-w