A BiLSTM-Based Hybrid Ensemble Approach for Forecasting Suspended Sediment Concentrations: Application to the Upper Yellow River

Jinsheng Fan, Renzhi Li (), Mingmeng Zhao and Xishan Pan
Additional contact information
Jinsheng Fan: School of Computer Science and Technology, Zhoukou Normal University, Zhoukou 466001, China
Renzhi Li: National Institute of Natural Hazards, Ministry of Emergency Management of the People’s Republic of China, Beijing 100085, China
Mingmeng Zhao: School of Computer Science and Technology, Zhoukou Normal University, Zhoukou 466001, China
Xishan Pan: Tidal Flat Research Center of Jiangsu Province, Nanjing 210036, China

Land, 2025, vol. 14, issue 6, 1-29

Abstract: Accurately predicting suspended sediment concentrations (SSC) is vital for effective reservoir planning, water resource optimization, and ecological restoration. This study proposes a hybrid ensemble model—VMD-MGGP-NGO-BiLSTM-NGO—which integrates Variational Mode Decomposition (VMD) for signal decomposition, Multi-Gene Genetic Programming (MGGP) for feature filtering, and a double-optimized NGO-BiLSTM-NGO (Northern Goshawk Optimization) structure for enhanced predictive learning. The model was trained and validated using daily discharge and SSC data from the Tangnaihai Hydrological Station on the upper Yellow River. The main findings are as follows: (1) The proposed model achieved an NSC improvement of 19.93% over the Extreme Gradient Boosting (XGBoost) and 15.26% over the Convolutional Neural Network—Long Short-Term Memory network (CNN-LSTM). (2) Compared to GWO- and PSO-based BiLSTM ensembles, the NGO-optimized VMD-MGGP-NGO- BiLSTM-NGO model achieved superior accuracy and robustness, with an average testing-phase NSC of 0.964, outperforming the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) counterparts. (3) On testing data, the model attained an NSC of 0.9708, indicating strong generalization across time. Overall, the VMD-MGGP-NGO-BiLSTM-NGO model demonstrates outstanding predictive capacity and structural synergy, serving as a reliable reference for future research on SSC forecasting and environmental modeling.

Keywords: BiLSTM; daily SSC prediction; ensemble learning; NGO; VMD; the upper Yellow River (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/6/1199/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/6/1199/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:6:p:1199-:d:1671268

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().