Indian Journal of Science and Technology

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Suspended Sediment Concentration Estimation Using Artificial Neural Networks and Neural-fuzzy Inference System Case Study: Karaj Dam


Affiliations
1 Civil Engineering Department, Engineering Faculty, Sistan and Balouchestan University, Zahedan, Iran, Islamic Republic of
 

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
User

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  • Suspended Sediment Concentration Estimation Using Artificial Neural Networks and Neural-fuzzy Inference System Case Study: Karaj Dam

Abstract Views: 537  |  PDF Views: 146

Authors

Abdolmajid Muhammadi
Civil Engineering Department, Engineering Faculty, Sistan and Balouchestan University, Zahedan, Iran, Islamic Republic of
Gholamhossein Akbari
Civil Engineering Department, Engineering Faculty, Sistan and Balouchestan University, Zahedan, Iran, Islamic Republic of
Gholamreza Azizzian
Civil Engineering Department, Engineering Faculty, Sistan and Balouchestan University, Zahedan, Iran, Islamic Republic of

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

References





DOI: https://doi.org/10.17485/ijst%2F2012%2Fv5i8%2F30538