Abstract
Logical development and effective use of water resources depend heavily on the practicability of runoff forecast. A monthly runoff interval prediction approach based on WOA-VMD-LSTM and non-parametric kernel density estimation is proposed in an effort to address the issue that conventional point prediction method cannot properly convey the uncertainty of prediction findings. Using variational modal decomposition (VMD) optimized by whale optimization algorithm (WOA), monthly runoff series is first divided into a number of relatively stable subsequences. Each subsequence is then predicted using long-term and short-term memory neural network (LSTM), and final point prediction results are obtained by superposition. Non-parametric kernel density estimation is then used to forecast monthly runoff interval and is compared with LSTM, EMD-LSTM, and VMD-LSTM models based on findings of point prediction. Findings indicate that this model’s prediction accuracy is noticeably higher than that of other models utilized in this paper. A realistic fluctuation range for runoff interval prediction is also provided by non-parametric kernel density estimation, which can be a useful reference for decision-makers in water resources management.
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We gratefully acknowledge the critical comments and corrections of the anonymous reviewers and editors, which improved the presentation and quality of the paper considerably.
Funding
This work was supported by Special project for collaborative innovation of science and technology in 2021 (No: 202121206) and Henan Province University Scientific and Technological Innovation Team (No: 18IRTSTHN009).
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All authors contributed to the study Conceptualization and Methodology. Writing - original draft preparation, data collection and analysis were performed by Wen-chuan Wang, Bo Wang, Kwok-wing Chau and Dong-mei Xu. All authors read and approved the final manuscript.
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Wang, Wc., Wang, B., Chau, Kw. et al. Monthly runoff time series interval prediction based on WOA-VMD-LSTM using non-parametric kernel density estimation. Earth Sci Inform 16, 2373–2389 (2023). https://doi.org/10.1007/s12145-023-01038-z
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DOI: https://doi.org/10.1007/s12145-023-01038-z