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

Yongtao Wang (), Jian Liu (), Rong Li (), Xinyu Suo () and EnHui Lu ()
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Yongtao Wang: Hunan University
Jian Liu: Hunan University
Rong Li: Hunan University
Xinyu Suo: Hunan University
EnHui Lu: Hunan University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 3, No 12, 987 pages

Abstract: 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.

Keywords: Wavelet decomposition; PS-SVMO; AB-BPC neural network; Combination model; Precipitation forecast (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03063-x

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