Suspended sediment concentration estimation using artificial neural networks and neural-fuzzy inference system case study: Karaj Dam

Abdolmajid Muhammadi; Gholamhossein Akbari and Gholamreza Azizzian

doi:10.17485/ijst/2012/v5i8.6

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Suspended sediment concentration estimation using artificial neural networks and neural-fuzzy inference system case study: Karaj Dam

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2012/v5i8.6

Year: 2012, Volume: 5, Issue: 8, Pages: 1-6

Original Article

Suspended sediment concentration estimation using artificial neural networks and neural-fuzzy inference system case study: Karaj Dam

Abdolmajid Muhammadi^*, Gholamhossein Akbari and Gholamreza Azizzian

Civil Engineering Department, Engineering Faculty, Sistan and Balouchestan University, Zahedan, Iran. [email protected]*
*Author For Correspondence
Abdolmajid Muhammadi
Civil Engineering Department
Email: [email protected]*

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Simulation and sediment assessment of the river are of the significant and practical issues in water resource management. To estimate the suspended sediment concentration of Karaj dam in this study, simultaneous water discharge data, base, water temperature and sediment density of Siraa Station located at Karaj dock entry have been used. Artificial Neural Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Sediment Rating Curve (SRC) modeling was used. Correlation coefficient (R) and Root Mean Square Error (RMSE) are considered the model's assessment criteria. The results show a higher accuracy of fuzzy model assessments in comparison with neural networks and sediment rating curve assessments.
Keywords: Sediment, Neural-fuzzy inference system, artificial neural network, Sediment rating curve