ESTIMATION OF SUSPENDED SEDIMENT USING ARTIFICIAL NEURAL NETWORK (CASE STUDY: JAMISHANWATERSHED IN KERMANSHAH)

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DASTORANI M.T.; AZIMI FASHI KH.; TALEBI A.; EKHTESASI M.R.

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Journal: JOURNAL OF WATERSHED MANAGEMENT RESEARCH Year:2013 | Volume:3 | Issue:6 Page(s): 61-74

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Information Journal Paper

Title

ESTIMATION OF SUSPENDED SEDIMENT USING ARTIFICIAL NEURAL NETWORK (CASE STUDY: JAMISHANWATERSHED IN KERMANSHAH)

Author(s)

DASTORANI M.T. | AZIMI FASHI KH. | TALEBI A. | EKHTESASI M.R. | Issue Writer Certificate

Keywords

SUSPENDED SEDIMENTQ2

ARTIFICIAL NEURAL NETWORKSQ2

JAMISHAN WATERSHEDQ2

SEDIMENT RATING CURVEQ2

Abstract

Erosion and sediment transport in the rivers is one of the most important and complicated subjects in river engineering. These phenomena have specific effects on water quality, bed and bank scouring as well as considerable damages to water related structures and projects. Therefore, precise prediction of river sediment plays an important role in water resources management and planning as well as design and construction of hydraulic structures. In this research it has been tried to evaluate the efficiency of ARTIFICIAL NEURAL NETWORKS in prediction of SUSPENDED SEDIMENT. Using multi-layer perceptron neural network SUSPENDED SEDIMENT of Heydarabad station on Jamishan river of Kermanshah has been predicted, and the results have been compared to those of SEDIMENT RATING CURVE method. Then the strengths and limitations of these methods have been analysed. According to the results, ANN has presented acceptable predictions in Heydarabad station SUSPENDED SEDIMENT simulation in comparison to the SEDIMENT RATING CURVE method. The values of R2 for the results of ANN and the SEDIMENT RATING CURVE methods are respectively 0.92 and 0.83. However, it must be mentioned that ANN is also not able to predict the peak values with acceptable accuracy, which can be a weakness of this model.

Cites

SURVEY OF DEPTH CHANGES OF THE ZARIVAR INTERNATIONAL WETLAND USING REMOTE SENSING AND ARTIFICIAL NEURAL NETWORK

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APA: Copy

DASTORANI, M.T., AZIMI FASHI, KH., TALEBI, A., & EKHTESASI, M.R.. (2013). ESTIMATION OF SUSPENDED SEDIMENT USING ARTIFICIAL NEURAL NETWORK (CASE STUDY: JAMISHANWATERSHED IN KERMANSHAH). JOURNAL OF WATERSHED MANAGEMENT RESEARCH, 3(6), 61-74. SID. https://sid.ir/paper/230347/en

Vancouver: Copy

DASTORANI M.T., AZIMI FASHI KH., TALEBI A., EKHTESASI M.R.. ESTIMATION OF SUSPENDED SEDIMENT USING ARTIFICIAL NEURAL NETWORK (CASE STUDY: JAMISHANWATERSHED IN KERMANSHAH). JOURNAL OF WATERSHED MANAGEMENT RESEARCH[Internet]. 2013;3(6):61-74. Available from: https://sid.ir/paper/230347/en

IEEE: Copy

M.T. DASTORANI, KH. AZIMI FASHI, A. TALEBI, and M.R. EKHTESASI, “ESTIMATION OF SUSPENDED SEDIMENT USING ARTIFICIAL NEURAL NETWORK (CASE STUDY: JAMISHANWATERSHED IN KERMANSHAH),” JOURNAL OF WATERSHED MANAGEMENT RESEARCH, vol. 3, no. 6, pp. 61–74, 2013, [Online]. Available: https://sid.ir/paper/230347/en

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