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Medium and Long-term Precipitation Prediction Using Wavelet Decomposition-prediction-reconstruction Model

Author

Listed:
  • Yongtao Wang

    (Hunan University
    Guizhou Institute of Water Resources Science)

  • Jian Liu

    (Hunan University)

  • Rong Li

    (Hunan University)

  • Xinyu Suo

    (Hunan University)

  • EnHui Lu

    (Hunan University)

Abstract

To improve the accuracy of medium and long-term precipitation prediction, we propose an innovative application of the wavelet decomposition-prediction-reconstruction model (WDPRM) herein. The model consists of wavelet decomposition, a particle swarm optimization support vector machine (PSO-SVM), and an artificial bee colony algorithm-optimized BP neural network (ABC-BP). First, the wavelet decomposition method is used to decompose the non-stationary precipitation time series into multiple decomposition terms with different frequencies. Second, the high-frequency component is predicted and verified by a PSO-SVM, while the low-frequency component is predicted and verified by the ABC-BP. Thirdly, the prediction results of the high- and low-frequency components are superimposed to obtain the final prediction result. The validity of the WDPRM is verified by using precipitation data from the Wujiang River Basin in Guizhou Province from 1961 to 2018. Compared to single prediction methods using BP or PSO, the WDPRM has the advantages of low mean absolute percentage error (MAPE) and root mean square error (RMSE), high $$\alpha$$ α and $$\Omega$$ Ω , and higher prediction accuracy. The precipitation forecast, and drought assessment for the next 10 years (2019–2028), have been completed. This research can effectively guide regional flood control, drought relief, and water resource allocation and dispatch.

Suggested Citation

  • Yongtao Wang & Jian Liu & Rong Li & Xinyu Suo & EnHui Lu, 2022. "Medium and Long-term Precipitation Prediction Using Wavelet Decomposition-prediction-reconstruction Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 971-987, February.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:3:d:10.1007_s11269-022-03063-x
    DOI: 10.1007/s11269-022-03063-x
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    References listed on IDEAS

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    1. Yan-Fang Sang, 2012. "A Practical Guide to Discrete Wavelet Decomposition of Hydrologic Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(11), pages 3345-3365, September.
    2. Xin Liu & Xuefeng Sang & Jiaxuan Chang & Yang Zheng, 2021. "Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4021-4041, September.
    3. Zaher Mundher Yaseen & Minglei Fu & Chen Wang & Wan Hanna Melini Wan Mohtar & Ravinesh C. Deo & Ahmed El-shafie, 2018. "Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1883-1899, March.
    4. Erhao Meng & Shengzhi Huang & Qiang Huang & Wei Fang & Hao Wang & Guoyong Leng & Lu Wang & Hao Liang, 2021. "A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1321-1337, March.
    5. Adnan Bashir & Muhammad Ahmed Shehzad & Ijaz Hussain & Muhammad Ishaq Asif Rehmani & Sajjad Haider Bhatti, 2019. "Reservoir Inflow Prediction by Ensembling Wavelet and Bootstrap Techniques to Multiple Linear Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5121-5136, December.
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    Cited by:

    1. Shengli Liao & Huan Wang & Benxi Liu & Xiangyu Ma & Binbin Zhou & Huaying Su, 2023. "Runoff Forecast Model Based on an EEMD-ANN and Meteorological Factors Using a Multicore Parallel Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1539-1555, March.
    2. Djerbouai Salim & Souag-Gamane Doudja & Ferhati Ahmed & Djoukbala Omar & Dougha Mostafa & Benselama Oussama & Hasbaia Mahmoud, 2023. "Comparative Study of Different Discrete Wavelet Based Neural Network Models for long term Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1401-1420, February.
    3. Haibo Gong & Fusheng Jiao & Li Cao & Huiyu Liu, 2022. "Long-term Precipitation Estimation Combining Time-Series Retrospective Forecasting and Downscaling-Calibration Procedure," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3087-3106, July.
    4. Bulent Haznedar & Huseyin Cagan Kilinc, 2022. "A Hybrid ANFIS-GA Approach for Estimation of Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4819-4842, September.

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