THE EVALUATION OF THE CPRECIP’S PARAMETER ABILITY ON APPROPRIATION OF SNOW’S EFFECT IN RIVER DAILY DISCHARGE PREDICTION BY NEURAL NETWORK AND FUZZY NEURAL NETWORK

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SEYYED ABBASI S.M.; GORAN ORIMI M.; FARID HOSSEINI A.; SHARIFI M.B.

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Journal: Journal of Irrigation Sciences and Engineering Year:2012 | Volume:35 | Issue:1 Page(s): 83-91

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Title

THE EVALUATION OF THE CPRECIP’S PARAMETER ABILITY ON APPROPRIATION OF SNOW’S EFFECT IN RIVER DAILY DISCHARGE PREDICTION BY NEURAL NETWORK AND FUZZY NEURAL NETWORK

Author(s)

SEYYED ABBASI S.M. | GORAN ORIMI M. | FARID HOSSEINI A. | SHARIFI M.B. | Issue Writer Certificate

Keywords

DISCHARGE PREDICTIONQ2

SNOWQ3

ARTIFICIAL NEURAL NETWORKQ2

FUZZY NEURAL NETWORKQ2

Abstract

One of the most effective parameters in DISCHARGE PREDICTION especially in SNOWy basins, is the SNOW parameter. SNOW water equivalent (SWE) is the most common parameter used in modeling river flow to take the effect of the SNOW on the model into account. In this research, according to unavailability of the SWE parameter in most of the basins, we tried to offer Cprecip and MAZ-Cprecip instead of SWE. Cprecip is cumulative precipitation from November 1st to April 1st indicating the SNOWpack amount melting in the spring. MAZ-Cprecip is received by giving some changes on the Cprecip (to be suitable with Mazandaran basins). The results showed that the Cprecip parameter can be replaced with SWE and that MAZ-Cprecip parameter is more efficient than Cprecip parameter in Mazandaran basins, due to its most conformity with the basins in this region.

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APA: Copy

SEYYED ABBASI, S.M., GORAN ORIMI, M., FARID HOSSEINI, A., & SHARIFI, M.B.. (2012). THE EVALUATION OF THE CPRECIP’S PARAMETER ABILITY ON APPROPRIATION OF SNOW’S EFFECT IN RIVER DAILY DISCHARGE PREDICTION BY NEURAL NETWORK AND FUZZY NEURAL NETWORK. IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE), 35(1), 83-91. SID. https://sid.ir/paper/217226/en

Vancouver: Copy

SEYYED ABBASI S.M., GORAN ORIMI M., FARID HOSSEINI A., SHARIFI M.B.. THE EVALUATION OF THE CPRECIP’S PARAMETER ABILITY ON APPROPRIATION OF SNOW’S EFFECT IN RIVER DAILY DISCHARGE PREDICTION BY NEURAL NETWORK AND FUZZY NEURAL NETWORK. IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE)[Internet]. 2012;35(1):83-91. Available from: https://sid.ir/paper/217226/en

IEEE: Copy

S.M. SEYYED ABBASI, M. GORAN ORIMI, A. FARID HOSSEINI, and M.B. SHARIFI, “THE EVALUATION OF THE CPRECIP’S PARAMETER ABILITY ON APPROPRIATION OF SNOW’S EFFECT IN RIVER DAILY DISCHARGE PREDICTION BY NEURAL NETWORK AND FUZZY NEURAL NETWORK,” IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE), vol. 35, no. 1, pp. 83–91, 2012, [Online]. Available: https://sid.ir/paper/217226/en

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