Compound Hydrological Forecasting Model by Long Short-term Memory Network Coupled with Adaptive Mode Decomposition and Evolutionary Algorithm

Zhong-kai Feng, Wen-jie Liu, Wen-jing Niu (), Tao Yang, Wen-chuan Wang and Sen Wang
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Zhong-kai Feng: Hohai University
Wen-jie Liu: Hohai University
Wen-jing Niu: Bureau of Hydrology, ChangJiang Water Resources Commission
Tao Yang: Hohai University
Wen-chuan Wang: North China University of Water Resources and Electric Power
Sen Wang: Hohai University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 6, No 11, 2672 pages

Abstract: Abstract Owing to increasing human activities and climate events over the past few decades, reliable hydrological forecasting has become a critical yet challenging task in water resources planning, reservoir operation, and water supply management. This study presents a hybrid artificial intelligence-based approach for hydrological prediction, combining signal decomposition techniques and optimization algorithms. Specifically, Variational Mode Decomposition (VMD) is employed to capture the irregular and dynamic features of the original hydrological time series. Subsequently, a Long Short-Term Memory (LSTM) network is applied to develop an effective forecasting model for each decomposed component, with the Whale Optimization Algorithm (WOA) employed to identify the optimal parameter set for each LSTM model. The proposed method is tested on runoff datasets from three hydrological stations in China, and its feasibility is demonstrated through several quantitative performance metrics. Extensive simulations reveal that the proposed model outperforms traditional models across various scenarios. With high accuracy and stability, this data-driven approach provides a robust solution to the complex problem of hydrological forecasting. Furthermore, the integration of signal decomposition and evolutionary algorithms enhances the performance of artificial intelligence models in regression tasks.

Keywords: Hydrological time series prediction; Signal decomposition; Long short-term memory network; Artificial intelligence; Evolutionary algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-024-04083-5

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