Rainfall Forecasting Using a BiLSTM Model Optimized by an Improved Whale Migration Algorithm and Variational Mode Decomposition

Yueqiao Yang, Shichuang Li, Ting Zhou (), Liang Zhao, Xiao Shi and Boni Du
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Yueqiao Yang: School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China
Shichuang Li: School of Information Management, Institute of Disaster Prevention, Langfang 065201, China
Ting Zhou: China International Engineering Consulting Corporation, Ecological Technical Research Institute (Beijing) Co., Ltd., Beijing 100048, China
Liang Zhao: School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Xiao Shi: China International Engineering Consulting Corporation, Ecological Technical Research Institute (Beijing) Co., Ltd., Beijing 100048, China
Boni Du: School of Emergency Management, Institute of Disaster Prevention, Langfang 065201, China

Mathematics, 2025, vol. 13, issue 15, 1-27

Abstract: The highly stochastic nature of rainfall presents significant challenges for the accurate prediction of its time series. To enhance the prediction performance of non-stationary rainfall time series, this study proposes a hybrid deep learning forecasting framework—VMD-IWMA-BiLSTM—that integrates Variational Mode Decomposition (VMD), Improved Whale Migration Algorithm (IWMA), and Bidirectional Long Short-Term Memory network (BiLSTM). Firstly, VMD is employed to decompose the original rainfall series into multiple modes, extracting Intrinsic Mode Functions (IMFs) with more stable frequency characteristics. Secondly, IWMA is utilized to globally optimize multiple hyperparameters of the BiLSTM model, enhancing its ability to capture complex nonlinear relationships and long-term dependencies. Finally, experimental validation is conducted using daily rainfall data from 2020 to 2024 at the Xinzheng National Meteorological Observatory. The results demonstrate that the proposed framework outperforms traditional models such as LSTM, ARIMA, SVM, and LSSVM in terms of prediction accuracy. This research provides new insights and effective technical pathways for improving rainfall time series prediction accuracy and addressing the challenges posed by high randomness.

Keywords: improved whale migration algorithm; variational mode decomposition; rainfall forecasting; hyperparameter optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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