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Uncertainty Analysis in Sediment Load Modeling Using ANN and SWAT Model

Author

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  • Mansour Talebizadeh
  • Saeid Morid
  • Seyyed Ayyoubzadeh
  • Mehdi Ghasemzadeh

Abstract

Sediment load estimation is essential in many water resources projects. In this study, the capability of two different types of model including SWAT as a process-based model and ANNs as a data-driven model in simulating sediment load were evaluated. The issue of uncertainty in the simulated outputs of the two models which stems from different sources was also investigated. Calibration and uncertainty analysis of SWAT were performed using monthly observed discharge and sediment load values and through the application of SUFI-2 procedure. The issue of uncertainty in the ANN model was also accounted for by training a network several times with different initial weights and bias values as well as randomly-selected training and validation sets, each time a network trained. Trying different input variables to find the best and most efficient network structure, it was found that in the forested watershed of Kasilian, adding average daily rainfall or previous values of discharge dose not change the performance of the ANN model significantly. Comparing the results of SWAT and ANN, it was found that SWAT model has a superior performance in estimating high values of sediment load, whereas ANN model estimated low and medium values more accurately. Moreover, prediction interval for the results of ANN was narrower than that of SWAT which suggests that ANN outputs are with less uncertainty. Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • Mansour Talebizadeh & Saeid Morid & Seyyed Ayyoubzadeh & Mehdi Ghasemzadeh, 2010. "Uncertainty Analysis in Sediment Load Modeling Using ANN and SWAT Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(9), pages 1747-1761, July.
    • Handle: RePEc:spr:waterr:v:24:y:2010:i:9:p:1747-1761
    DOI: 10.1007/s11269-009-9522-2
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    References listed on IDEAS

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    1. Raveendra Rai & B. Mathur, 2008. "Event-based Sediment Yield Modeling using Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(4), pages 423-441, April.
    2. Ashok Mishra & S. Kar & V. Singh, 2007. "Prioritizing Structural Management by Quantifying the Effect of Land Use and Land Cover on Watershed Runoff and Sediment Yield," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(11), pages 1899-1913, November.
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    2. J. Yazdi & A . Moridi, 2017. "Interactive Reservoir-Watershed Modeling Framework for Integrated Water Quality Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2105-2125, May.
    3. Seyed Morteza Seyedian & Ozgur Kisi & Abbas Parsaie & Mojtaba Kashani, 2024. "Improving the Reliability of Compound Channel Discharge Prediction Using Machine Learning Techniques and Resampling Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4685-4709, September.
    4. Pawan Kumar Rai & C. T. Dhanya & B. R. Chahar, 2018. "Coupling of 1D models (SWAT and SWMM) with 2D model (iRIC) for mapping inundation in Brahmani and Baitarani river delta," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(3), pages 1821-1840, July.
    5. Vanessa Sari & Nilza Maria Reis Castro & Olavo Correa Pedrollo, 2017. "Estimate of Suspended Sediment Concentration from Monitored Data of Turbidity and Water Level Using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4909-4923, December.
    6. M. Mustafa & R. Rezaur & S. Saiedi & M. Isa, 2012. "River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 1879-1897, May.
    7. Wei Zhang & Tian Li, 2015. "The Influence of Objective Function and Acceptability Threshold on Uncertainty Assessment of an Urban Drainage Hydraulic Model with Generalized Likelihood Uncertainty Estimation Methodology," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 2059-2072, April.
    8. Hamideh Hosseini Safa & Saeed Morid & Mahnoush Moghaddasi, 2012. "Incorporating Economy and Long-term Inflow Forecasting Uncertainty into Decision-making for Agricultural Water Allocation during Droughts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2267-2281, June.
    9. Shirin Karimi & Bahman Jabbarian Amiri & Arash Malekian, 2019. "Similarity Metrics-Based Uncertainty Analysis of River Water Quality Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 1927-1945, April.
    10. Tarate Suryakant Bajirao & Pravendra Kumar & Manish Kumar & Ahmed Elbeltagi & Alban Kuriqi, 2021. "Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers," Sustainability, MDPI, vol. 13(2), pages 1-29, January.
    11. Aytac Guven & Özgür Kişi, 2011. "Estimation of Suspended Sediment Yield in Natural Rivers Using Machine-coded Linear Genetic Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 691-704, January.
    12. Halecki, Wiktor & Kruk, Edyta & Ryczek, Marek, 2018. "Loss of topsoil and soil erosion by water in agricultural areas: A multi-criteria approach for various land use scenarios in the Western Carpathians using a SWAT model," Land Use Policy, Elsevier, vol. 73(C), pages 363-372.

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