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Streamflow Data Infilling Using Machine Learning Techniques with Gamma Test

Author

Listed:
  • Saad Dahmani

    (Université de Bouira)

  • Sarmad Dashti Latif

    (Scientifc Research Center, Soran University
    Komar University of Science and Technology)

Abstract

Length, completeness, and quality of hydrological time-series can affect considerably the efficiency of decisions in water resources engineering. Regrettably, obtaining short, incomplete, and low-quality data is not rare. In this study, different machine learning techniques have been implemented and applied to fill in missed data of streamflow at Coxs River, in Australia. The implemented techniques are Support Vector Regression improved by Equilibrium Optimizer (SVR-EO) and Particle Swarm Optimizer (SVR-PSO), alongside Artificial Neural Networks trained by EO (ANN-EO) and PSO (ANN-PSO). Multivariate Adaptive Regression Splines (MARS) and Multiple Linear Regression (MLR) have been used for comparison purposes. Rainfall data provided by five climatic stations located near Coxs River along with Kowmung River streamflow records have been used to fill the gaps in the Coxs River time-series. The gamma test has been used to select the convenient data combination that reduces errors in prediction models. According to the findings, SVR-PSO and SVR-EO outperformed the other techniques with $$R^{2}\approx 0.94$$ R 2 ≈ 0.94 for training, and $$R^{2}\approx 0.85$$ R 2 ≈ 0.85 for testing part. The imputation process and the developed SVR-EO and SVR-PSO could be applied to other rivers in different countries to ensure whether these methods could be generalized.

Suggested Citation

  • Saad Dahmani & Sarmad Dashti Latif, 2024. "Streamflow Data Infilling Using Machine Learning Techniques with Gamma Test," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(2), pages 701-716, January.
  • Handle: RePEc:spr:waterr:v:38:y:2024:i:2:d:10.1007_s11269-023-03694-8
    DOI: 10.1007/s11269-023-03694-8
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    References listed on IDEAS

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    1. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.
    2. Thakolpat Khampuengson & Wenjia Wang, 2023. "Novel Methods for Imputing Missing Values in Water Level Monitoring Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 851-878, January.
    3. Jenq-Tzong Shiau & Hui-Ting Hsu, 2016. "Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin, Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1499-1513, March.
    4. Tomasz Niedzielski & Michał Halicki, 2023. "Improving Linear Interpolation of Missing Hydrological Data by Applying Integrated Autoregressive Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(14), pages 5707-5724, November.
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    Cited by:

    1. Malihe Danesh & Amin Gharehbaghi & Saeid Mehdizadeh & Amirhossein Danesh, 2025. "A Comparative Assessment of Machine Learning and Deep Learning Models for the Daily River Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(4), pages 1911-1930, March.

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