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An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models

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  • Meral Buyukyildiz

    (University of Selcuk)

  • Serife Yurdagul Kumcu

    (University of Necmettin Erbakan)

Abstract

Sediment transport in streams and rivers takes two forms as suspended load and bed load. Suspended load comprises sand + silt + clay-sized particles that are held in suspension due to the turbulence and will only settle when the stream velocity decreases, such as when the streambed becomes flatter, or the streamflow into a pond or lake. The sources of the suspended sediments are the sediments transported from the river basin by runoff or wind and the eroded sediments of the river bed and banks. Suspended-sediment load is a key indicator for assessing the effect of land use changes, water quality studies and engineering practices in watercourses. Measuring suspended sediment in streams is real sampling and the collection process is both complex and expensive. In recent years, artificial intelligence methods have been used as a predictor for hydrological phenomenon namely to estimate the amount of suspended sediment. In this paper the abilities of Support Vector Machine (SVM), Artificial Neural Networks (ANNs) and Adaptive Network Based Fuzzy Inference System (ANFIS) models among the artificial intelligence methods have been investigated to estimate the suspended sediment load (SSL) in Ispir Bridge gauging station on Coruh River (station number: 2316). Coruh River is located in the northern east part of Turkey and it is one of the world”s the fastest, the deepest and the largest rivers of the Coruh Basin. In this study, in order to estimate the suspended sediment load, different combinations of the streamflow and the SSL were used as the model inputs. Its results accuracy was compared with the results of conventional correlation coefficient analysis between input and output variables and the best combination was identified. Finally, in order to predict SSL, the SVM, ANFIS and various ANNs models were used. The reliability of SVM, ANFIS and ANN models were determined based on performance criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Efficiency Coefficient (EC) and Determination Coefficient (R2).

Suggested Citation

  • Meral Buyukyildiz & Serife Yurdagul Kumcu, 2017. "An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1343-1359, March.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:4:d:10.1007_s11269-017-1581-1
    DOI: 10.1007/s11269-017-1581-1
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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. Isa Ebtehaj & Hossein Bonakdari, 2014. "Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4765-4779, October.
    3. Manish Goyal, 2014. "Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1991-2003, May.
    4. Vahid Nourani & Farhad Alizadeh & Kiyoumars Roushangar, 2016. "Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 393-407, January.
    5. Vahid Nourani & Farhad Alizadeh & Kiyoumars Roushangar, 2016. "Evaluation of a Two-Stage SVM and Spatial Statistics Methods for Modeling Monthly River Suspended Sediment Load," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 393-407, January.
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    4. 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.
    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. Sarita Gajbhiye Meshram & Vijay P. Singh & Ozgur Kisi & Vahid Karimi & Chandrashekhar Meshram, 2020. "Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4561-4575, December.
    7. You-Da Jhong & Hsin-Ping Lin & Chang-Shian Chen & Bing-Chen Jhong, 2022. "Real-time Neural-network-based Ensemble Typhoon Flood Forecasting Model with Self-organizing Map Cluster Analysis: A Case Study on the Wu River Basin in Taiwan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3221-3245, July.
    8. Prince, & Hati, Ananda Shankar, 2021. "A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
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    10. Shahab Araghinejad & Nima Fayaz & Seyed-Mohammad Hosseini-Moghari, 2018. "Development of a Hybrid Data Driven Model for Hydrological Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3737-3750, September.
    11. Ashish Kumar & Pravendra Kumar & Vijay Kumar Singh, 2019. "Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1217-1231, February.

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