IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i15d10.1007_s11269-024-03943-4.html
   My bibliography  Save this article

Multimodal Fusion of Optimized GRU–LSTM with Self-Attention Layer for Hydrological Time Series Forecasting

Author

Listed:
  • Huseyin Cagan Kilinc

    (Istanbul Aydın University)

  • Sina Apak

    (Istanbul Aydın University)

  • Furkan Ozkan

    (Çukurova University)

  • Mahmut Esad Ergin

    (Istanbul Aydın University)

  • Adem Yurtsever

    (Istanbul University-Cerrahpaşa)

Abstract

Accurate flow forecasting is crucial for effective basin management, regional agricultural policy development, environmental impact analysis, soil and water conservation studies, and flood protection planning. This study proposes a novel approach that integrates particle swarm optimization (PSO) with bidirectional long short-term memory (Bi-LSTM) and bidirectional gated recurrent unit (Bi-GRU) architectures, augmented by feature fusion and attention layers. Our approach consistently outperforms traditional methods across multiple datasets, including Ahmethacı, Büyükincirli, and Ersil, thereby achieving lower RMSE, MAE, and higher KGE and BF scores. Specifically, in Ahmethacı, our method yields an RMSE of 3.448, MAE of 1.224, and an R2 of 0.886. In Büyükincirli, it records an RMSE of 0.085, MAE of 0.040, and an R2 of 0.964. In Ersil, it achieves an RMSE of 1.495, MAE of 0.565, and R2 of 0.883. These results underscore the effectiveness of the proposed approach in flow forecasting.

Suggested Citation

  • Huseyin Cagan Kilinc & Sina Apak & Furkan Ozkan & Mahmut Esad Ergin & Adem Yurtsever, 2024. "Multimodal Fusion of Optimized GRU–LSTM with Self-Attention Layer for Hydrological Time Series Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(15), pages 6045-6062, December.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:15:d:10.1007_s11269-024-03943-4
    DOI: 10.1007/s11269-024-03943-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-03943-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-024-03943-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fugang LI & Guangwen MA & Shijun CHEN & Weibin HUANG, 2021. "An Ensemble Modeling Approach to Forecast Daily Reservoir Inflow Using Bidirectional Long- and Short-Term Memory (Bi-LSTM), Variational Mode Decomposition (VMD), and Energy Entropy Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2941-2963, July.
    2. Manish Yadav & B. B. Vashisht & S. K. Jalota & T. Jyolsna & Samar Pal Singh & Arun Kumar & Amit Kumar & Gurjeet Singh, 2024. "Improving Water Efficiencies in Rural Agriculture for Sustainability of Water Resources: A Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(10), pages 3505-3526, August.
    3. Harshwardhan Yadav & Param Shah & Neel Gandhi & Tarjni Vyas & Anuja Nair & Shivani Desai & Lata Gohil & Sudeep Tanwar & Ravi Sharma & Verdes Marina & Maria Simona Raboaca, 2023. "CNN and Bidirectional GRU-Based Heartbeat Sound Classification Architecture for Elderly People," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramtin Moeini & Kamran Nasiri & Seyed Hossein Hosseini, 2024. "Predicting the Water Inflow Into the Dam Reservoir Using the Hybrid Intelligent GP-ANN- NSGA-II Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(11), pages 4137-4159, September.
    2. Sheng He & Xuefeng Sang & Junxian Yin & Yang Zheng & Heting Chen, 2023. "Short-term Runoff Prediction Optimization Method Based on BGRU-BP and BLSTM-BP Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 747-768, January.
    3. Zhong Huang & Linna Li & Guorong Ding, 2023. "A Daily Air Pollutant Concentration Prediction Framework Combining Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Network," Sustainability, MDPI, vol. 15(13), pages 1-22, July.
    4. Kang, Yan & Chen, Peiru & Cheng, Xiao & Zhang, Shuo & Song, Songbai, 2022. "Novel hybrid machine learning framework with decomposition–transformation and identification of key modes for estimating reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 273(C).
    5. Bao-Jian Li & Guo-Liang Sun & Yan Liu & Wen-Chuan Wang & Xu-Dong Huang, 2022. "Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2095-2115, April.
    6. M. Rajesh & Sachdeva Anishka & Pansari Satyam Viksit & Srivastav Arohi & S. Rehana, 2023. "Improving Short-range Reservoir Inflow Forecasts with Machine Learning Model Combination," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 75-90, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:38:y:2024:i:15:d:10.1007_s11269-024-03943-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.