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Developing Extended and Unscented Kalman Filter-Based Neural Networks to Predict Cluster-Induced Roughness in Gravel Bed Rivers

Author

Listed:
  • Masoud Karbasi

    (University of Zanjan
    University of Prince Edward Island)

  • Mohammad Ghasemian

    (University of Zanjan)

  • Mehdi Jamei

    (University of Prince Edward Island
    Shahid Chamran University of Ahvaz
    Al-Ayen University)

  • Anurag Malik

    (Punjab Agricultural University, Regional Research Station)

  • Ozgur Kisi

    (Lübeck University of Applied Science
    Ilia State University)

Abstract

Flow resistance in natural gravel-bed rivers must be precisely predicted in order for water-related infrastructure to be designed effectively. Cluster microforms are significant factors in determining the resistance of flow in rivers with gravel beds. To precisely estimate the cluster microform-induced Darcy-Weisbach roughness coefficient, the current study utilized two novel and robust data-intelligence paradigms: Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) -based Artificial Neural Networks (UKF-ANN and EKF-ANN), in addition to Response Surface Methodology (RSM) and Multi-layer Perceptron Neural Network (MLPNN). A total of 128 sets of laboratory data were used to develop the models, which encompassed a range of geometric and hydraulic scenarios. Various performance metrics, including, Mean Absolute Percentage Error (MAPE) Root Mean Square Error (RMSE) and Correlation Coefficient (R) were employed to assess the models' performance. The results showed that the implemented machine learning methods (i.e., MLPNN, UKF-ANN, EKF-ANN) had a good performance. Comparison of machine learning models showed that the EKF-ANN (R = 0.9747, MAPE = 7.73, RMSE = 0.0041) and UKF-ANN (R = 0.9617, MAPE = 8.17, RMSE = 0.0050) models provided higher accuracy compared to MLPNN (R = 0.940, MAPE = 11.38, RMSE = 0.0064,) and RSM (R = 0.957, MAPE = 11.02, RMSE = 0.0057). Moreover, the sensitivity analysis demonstrates that the roughness coefficient is primarily affected by the hydraulic radius to the longitudinal distance of clusters (R/λ).

Suggested Citation

  • Masoud Karbasi & Mohammad Ghasemian & Mehdi Jamei & Anurag Malik & Ozgur Kisi, 2024. "Developing Extended and Unscented Kalman Filter-Based Neural Networks to Predict Cluster-Induced Roughness in Gravel Bed Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(8), pages 3023-3048, June.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:8:d:10.1007_s11269-024-03803-1
    DOI: 10.1007/s11269-024-03803-1
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    References listed on IDEAS

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    1. Manish Pandey & Masoud Karbasi & Mehdi Jamei & Anurag Malik & Jaan H. Pu, 2023. "A Comprehensive Experimental and Computational Investigation on Estimation of Scour Depth at Bridge Abutment: Emerging Ensemble Intelligent Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3745-3767, July.
    2. H. Azamathulla & Robert Jarrett, 2013. "Use of Gene-Expression Programming to Estimate Manning’s Roughness Coefficient for High Gradient Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 715-729, February.
    3. Reza Piraei & Seied Hosein Afzali & Majid Niazkar, 2023. "Assessment of XGBoost to Estimate Total Sediment Loads in Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(13), pages 5289-5306, October.
    4. Bibhuti Bhusan Sahoo & Sovan Sankalp & Ozgur Kisi, 2023. "A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4271-4292, September.
    5. Vasileios Kitsikoudis & Epaminondas Sidiropoulos & Lazaros Iliadis & Vlassios Hrissanthou, 2015. "A Machine Learning Approach for the Mean Flow Velocity Prediction in Alluvial Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(12), pages 4379-4395, September.
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