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Performance Improvement of LSTM-based Deep Learning Model for Streamflow Forecasting Using Kalman Filtering

Author

Listed:
  • Fatemeh Bakhshi Ostadkalayeh

    (K. N. Toosi University of Technology)

  • Saba Moradi

    (University of Tehran)

  • Ali Asadi

    (Azad University)

  • Alireza Moghaddam Nia

    (University of Tehran)

  • Somayeh Taheri

    (University of Tehran)

Abstract

Prediction of streamflow as a crucial source of hydrological information plays a central role in various fields of water resources projects. While accurate daily streamflow forecasts are essential, predicting streamflow based on limited data is useful for minimizing computational time and supporting flood early warning systems. This study aims to improve Long Short-Term Memory (LSTM) performance by Kalman filter (KF) for streamflow forecasting. For this goal, the simulation has been specified according to the daily streamflow series for 60 years of Dez Dam, located in Iran. We compared the results of simulating the LSTM, LSTM-KF, LSTM-UKF, LSTM-KFS, and LSTM-UKFS models. In addition, the accuracy of the proposed method is evaluated with statistical analysis including Nash–Sutcliffe efficiency (NSE), root-mean-squared error (RMSE), average absolute relative error (AARE) and mean relative error (MAE). The results demonstrate that the LSTM-UKFS model is highly effective in flood forecasting, indicating the promising potential of a simple architecture deep-learning approach for predicting floods, even in the presence of dams in the study area.

Suggested Citation

  • Fatemeh Bakhshi Ostadkalayeh & Saba Moradi & Ali Asadi & Alireza Moghaddam Nia & Somayeh Taheri, 2023. "Performance Improvement of LSTM-based Deep Learning Model for Streamflow Forecasting Using Kalman Filtering," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 3111-3127, June.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:8:d:10.1007_s11269-023-03492-2
    DOI: 10.1007/s11269-023-03492-2
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    References listed on IDEAS

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    1. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    2. Yirui Wu & Yukai Ding & Yuelong Zhu & Jun Feng & Sifeng Wang, 2020. "Complexity to Forecast Flood: Problem Definition and Spatiotemporal Attention LSTM Solution," Complexity, Hindawi, vol. 2020, pages 1-13, March.
    3. Zaher Mundher Yaseen & Minglei Fu & Chen Wang & Wan Hanna Melini Wan Mohtar & Ravinesh C. Deo & Ahmed El-shafie, 2018. "Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1883-1899, March.
    4. Ahmad Khazaee Poul & Mojtaba Shourian & Hadi Ebrahimi, 2019. "A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2907-2923, June.
    5. Maryam Rahimzad & Alireza Moghaddam Nia & Hosam Zolfonoon & Jaber Soltani & Ali Danandeh Mehr & Hyun-Han Kwon, 2021. "Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4167-4187, September.
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    Cited by:

    1. Xin Fang & Jie Wu & Peiqi Jiang & Kang Liu & Xiaohua Wang & Sherong Zhang & Chao Wang & Heng Li & Yishu Lai, 2024. "A Rapid Assessment Method for Flood Risk Mapping Integrating Aerial Point Clouds and Deep Learning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(5), pages 1753-1772, March.
    2. Ziyu Li & Xianqi Zhang, 2024. "A Novel Coupled Model for Monthly Rainfall Prediction Based on ESMD-EWT-SVD-LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(9), pages 3297-3312, July.
    3. Sajjad M. Vatanchi & Hossein Etemadfard & Mahmoud F. Maghrebi & Rouzbeh Shad, 2023. "A Comparative Study on Forecasting of Long-term Daily Streamflow using ANN, ANFIS, BiLSTM and CNN-GRU-LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4769-4785, September.
    4. Fatemeh Ghobadi & Amir Saman Tayerani Charmchi & Doosun Kang, 2023. "Feature Extraction from Satellite-Derived Hydroclimate Data: Assessing Impacts on Various Neural Networks for Multi-Step Ahead Streamflow Prediction," Sustainability, MDPI, vol. 15(22), pages 1-32, November.

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