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Suspended Load Modeling of River Using Soft Computing Techniques

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  • Amir Moradinejad

    (Soil Conservation and Watershed Management Research Department, Markazi Agricultural and Natural Resources Research and Education Center, Arak, Agricultural Research Education & Extention Organization (AREEO))

Abstract

The phenomenon of sediment transport has always affected many river and civil structures. Not knowing the exact amount of sediment, causes much damage. Correct estimation of river sediment concentration is essential for planning and managing water resources projects and environmental issues. For this, you can use the artificial intelligence method, which has high flexibility. In this research, adaptive neuro-fuzzy models (ANFIS), gene expression programming (GEP), support vector regression (SVR), Group Method of Data Handling (GMDH), and the classical method of sediment rating curve (SRC) were used to model and prediction. For this purpose, the daily data of temperature, rainfall, sediment, and discharge of the Jalair station located in the Markazi province of Iran were used. The results obtained from these five methods were compared with each other and with the measured data. To evaluate the methods used, correlation coefficient, root mean square error, mean absolute error, and Taylor diagram were used. The results show the acceptable performance of data mining methods compared to the Sediment rating curve. Also, the model's superiority (GEP) was shown with the highest coefficient of determination R2 with a value of 0.98 and the lowest root mean square error RMSE in terms of tons per day with a value of 3721. The efficiency of the ANFIS and GMDH model with R2 values of 0.93, 0.98, and RMSE values of 16556, and 18638 was somewhat better than the SVR model with an R2 value of 0.90 and RMSE value of 35158.

Suggested Citation

  • Amir Moradinejad, 2024. "Suspended Load Modeling of River Using Soft Computing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(6), pages 1965-1986, April.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:6:d:10.1007_s11269-023-03722-7
    DOI: 10.1007/s11269-023-03722-7
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    References listed on IDEAS

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    1. Melesse, A.M. & Ahmad, S. & McClain, M.E. & Wang, X. & Lim, Y.H., 2011. "Suspended sediment load prediction of river systems: An artificial neural network approach," Agricultural Water Management, Elsevier, vol. 98(5), pages 855-866, March.
    2. 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.
    3. Ozgur Kisi & Coskun Ozkan, 2017. "Erratum to: A New Approach for Modeling Sediment-Discharge Relationship: Local Weighted Linear Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 25-25, January.
    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. Ozgur Kisi & Coskun Ozkan, 2017. "A New Approach for Modeling Sediment-Discharge Relationship: Local Weighted Linear Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 1-23, January.
    6. Siyamak Doroudi & Ahmad Sharafati & Seyed Hossein Mohajeri & Haitham Afan, 2021. "Estimation of Daily Suspended Sediment Load Using a Novel Hybrid Support Vector Regression Model Incorporated with Observer-Teacher-Learner-Based Optimization Method," Complexity, Hindawi, vol. 2021, pages 1-13, March.
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    Cited by:

    1. Nikolaos Efthimiou, 2025. "Suspended Load Estimation in Data Scarce Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(1), pages 311-378, January.
    2. Haitham Abdulmohsin Afan & Wan Hanna Melini Wan Mohtar & Muammer Aksoy & Ali Najah Ahmed & Faidhalrahman Khaleel & Md Munir Hayet Khan & Ammar Hatem Kamel & Mohsen Sherif & Ahmed El-Shafie, 2025. "A Multi-Functional Genetic Algorithm-Neural Network Model for Predicting Suspended Sediment Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(5), pages 2033-2048, March.

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