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River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia

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  • M. Mustafa
  • R. Rezaur
  • S. Saiedi
  • M. Isa

Abstract

Estimation of suspended sediment discharge in rivers has a vital role in dealing with water resources problems and hydraulic structures. In this study, a Multilayer Perceptron (MLP) feed forward neural network with four different training algorithms was used to predict the suspended sediment discharge of a river (Pari River at Silibin) in Peninsular Malaysia. The training algorithms are Gradient Descent (GD), Gradient Descent with Momentum (GDM), Scaled Conjugate Gradient (SCG), and Levenberg Marquardt (LM). Different statistical measures, time of convergence and number of epochs to reach the required accuracy were used to evaluate the performance of training algorithms. The analysis showed that SCG and LM performed better than GD and GDM. While the performance of the superior algorithms (i.e., SCG and LM) is similar, LM required considerably shorter time of convergence. It was concluded that both training algorithms SCG and LM could be recommended for suspended sediment prediction using MLP networks. However, LM was the faster (1/7 of SCG convergence time) of the two algorithms. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:26:y:2012:i:7:p:1879-1897
    DOI: 10.1007/s11269-012-9992-5
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    1. Rana Muhammad Adnan & Kulwinder Singh Parmar & Salim Heddam & Shamsuddin Shahid & Ozgur Kisi, 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    2. Vasileios Kitsikoudis & Epaminondas Sidiropoulos & Vlassios Hrissanthou, 2014. "Machine Learning Utilization for Bed Load Transport 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. 28(11), pages 3727-3743, September.
    3. Ozgur Kisi, 2015. "Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5109-5127, November.
    4. 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.
    5. C. Iglesias & J. Martínez Torres & P. García Nieto & J. Alonso Fernández & C. Díaz Muñiz & J. Piñeiro & J. Taboada, 2014. "Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 319-331, January.
    6. S. Aggarwal & Arun Goel & Vijay Singh, 2012. "Stage and Discharge Forecasting by SVM and ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(13), pages 3705-3724, October.
    7. 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.
    8. Gokmen Tayfur & Ata Nadiri & Asghar Moghaddam, 2014. "Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1173-1184, March.
    9. Ozgur Kisi & Mohammad Zounemat-Kermani, 2016. "Suspended Sediment Modeling Using Neuro-Fuzzy Embedded Fuzzy c-Means Clustering Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3979-3994, September.
    10. Hamid Moeeni & Hossein Bonakdari, 2018. "Impact of Normalization and Input on ARMAX-ANN Model Performance in 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. 32(3), pages 845-863, February.
    11. Vahid Nourani & Amir Molajou & Ali Davanlou Tajbakhsh & Hessam Najafi, 2019. "A Wavelet Based Data Mining Technique for Suspended Sediment Load Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1769-1784, March.
    12. Junyu Zhang & Dafang Fu & Christian Urich & Rajendra Prasad Singh, 2018. "Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning," Sustainability, MDPI, vol. 10(12), pages 1-16, December.

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