Particle swarm optimization based LSTM networks for water level forecasting: A case study on Bangladesh river network Article Swipe
Jannatul Ferdous Ruma
,
Mohammed Sarfaraz Gani Adnan
,
Ashraf Dewan
,
Rashedur M. Rahman
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2023.100951
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.rineng.2023.100951
Floods are one of the most catastrophic natural disasters. Water level forecasting is an essential method of avoiding floods and disaster preparedness. In recent years, models for predicting water levels have been developed using artificial intelligence techniques like the artificial neural network (ANN). It has been demonstrated that more advanced and sequenced-based deep learning techniques, like long short-term memory (LSTM) networks, are superior at forecasting hydrological data. However, historically, most LSTM hyperparameters were based on experience, which typically did not produce the best outcomes. The Particle Swarm Optimization (PSO) method was utilized to adjust the LSTM hyperparameter to increase the capacity to learn data sequence characteristics. Utilizing water level observation data from stations along Bangladesh's Brahmaputra, Ganges, and Meghna rivers, the model was utilized to estimate flood dynamics. The Nash Sutcliffe efficiency (NSE) coefficient, root mean square error (RMSE), and MAE were used to assess the model's performance, where PSO-LSTM model outperforms the ANN, PSO-ANN, and LSTM models in predicting water levels in all stations. The PSO-LSTM model provides improved prediction accuracy and stability and improves water level forecasting accuracy at varying lead times. The findings may aid in sustainable flood risk mitigation in the study region in the future.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1016/j.rineng.2023.100951
- OA Status
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- Cited By
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- References
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4319939964Canonical identifier for this work in OpenAlex
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- Title
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Particle swarm optimization based LSTM networks for water level forecasting: A case study on Bangladesh river networkWork title
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articleOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-02-10Full publication date if available
- Authors
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Jannatul Ferdous Ruma, Mohammed Sarfaraz Gani Adnan, Ashraf Dewan, Rashedur M. RahmanList of authors in order
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https://doi.org/10.1016/j.rineng.2023.100951Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1016/j.rineng.2023.100951Direct OA link when available
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Particle swarm optimization, Hyperparameter, Computer science, Mean squared error, Flood myth, Artificial neural network, Artificial intelligence, Recurrent neural network, Machine learning, Data mining, Statistics, Mathematics, Geography, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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77Total citation count in OpenAlex
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2025: 28, 2024: 37, 2023: 12Per-year citation counts (last 5 years)
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