Abstract
Precipitation, as an important indicator describing the evolution of the regional climate system, plays an important role in understanding the spatial and temporal distribution characteristics of regional precipitation. Scientific and accurate prediction of regional precipitation is helpful to provide theoretical basis for relevant departments to guide flood and drought control. To address the uncertainty and nonlinear characteristics of precipitation series, this paper uses the established improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)-wavelet signal denoising (WSD)-bi-directional long short-term memory (BiLSTM), and echo state network (ESN) models to predict precipitation of four cities in southern Anhui Province. The BiLSTM is used to predict the high-frequency components and the ESN to predict the low-frequency components, thus avoiding the influence between the two neural network predictions. The results show that the ICEEMDAN-WSD-BiLSTM and ESN models are more accurate. The average relative error reached 2.64% and the NSE (Nash–Sutcliffe efficiency coefficient) was 0.91, which was significantly better than the other four models. The model reveals the temporal change pattern and evolution characteristics of future precipitation, guides flood prevention and mitigation, and has certain theoretical significance and application value for promoting regional sustainable development.
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Data and materials are available from the corresponding author upon request.
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This work was supported by the Key Scientific Research Project of Colleges and Universities in Henan Province (CN) [grant number 17A570004].
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All authors contributed to the study conception and design. writing and editing: Xianqi Zhang and Haiyang Chen; chart editing: Yihao Wen; preliminary data collection: Jingwen Shi, Yimeng xiao. All authors read and approved the final manuscript.
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Zhang, X., Chen, H., Wen, Y. et al. A new rainfall prediction model based on ICEEMDAN-WSD-BiLSTM and ESN. Environ Sci Pollut Res 30, 53381–53396 (2023). https://doi.org/10.1007/s11356-023-25906-9
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DOI: https://doi.org/10.1007/s11356-023-25906-9