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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
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    References listed on IDEAS

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    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. Reza Rezaiy & Ani Shabri, 2024. "Improving Drought Prediction Accuracy: A Hybrid EEMD and Support Vector Machine Approach with Standardized Precipitation Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5255-5277, October.
    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.
    4. S. Khorram & N. Jehbez, 2023. "A Hybrid CNN-LSTM Approach for Monthly Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 4097-4121, August.
    5. 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.
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