Abstract
Precise rainfall forecasting modeling assumes a pivotal role in water resource planning and management. Conducting a comprehensive analysis of the rainfall time series and making appropriate adjustments to the forecast model settings based on the characterization results of the rainfall series significantly enhance the accuracy of the forecast model. This paper employed the Mann–Kendall and sliding T mutation tests to identify the mutational components in rainfall between 1961 and 2013 at four meteorological stations located in Henan Province. Wavelet analysis was utilized to determine the periodicity of the precipitation series. The model parameters were adjusted based on the mutation and periodicity findings, and appropriate training and test sets were selected accordingly. Rainfall simulation in Henan Province, China, was conducted using a combination of complementary ensemble empirical mode decomposition (CEEMD) and bi-directional long short-term memory (BiLSTM) networks. The integrated approach aimed at predicting rainfall in the region. The findings of this study demonstrate that the CEEMD-BiLSTM model, coupled with feature analysis, yielded favorable results in terms of prediction accuracy. The model achieved a mean MAE (mean absolute error) of 131.210, a mean MRE (mean relative error) of 0.637, a mean RMSE (root mean square error) of 187.776, and an NSE (Nash–Sutcliffe efficiency) above 0.910. The data processed according to the feature analysis results and then predicted; Zhengzhou, Anyang, Lushi, and Xinyang stations improved by 39.548%, 14.478%, 11.548%, and 19.037% respectively compared with the original prediction model.
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This manuscript was supported by the Innovation Fund for Doctoral Students of North China University of Water Resources and Electric Power. The grant number is NCWUBC202303.
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All authors contributed to the study conception and design. Supervision, writing—review and editing of the manuscript is the responsibility of [Xianqi Zhang]. The first draft of the manuscript was written by [Zhiwen Zheng]. Data curation and investigation were performed by [Haiyang Li]. Methodology and software were written by [Fang Liu]. Validation and visualization were written by [Qiuwen Yin], and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Zhang, X., Zheng, Z., Li, H. et al. Deep learning precipitation prediction models combined with feature analysis. Environ Sci Pollut Res 30, 121948–121959 (2023). https://doi.org/10.1007/s11356-023-30833-w
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DOI: https://doi.org/10.1007/s11356-023-30833-w