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Cites:

1

Information Journal Paper

Title

APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR RUNOFF ESTIMATION (CASE STUDY: PLASJAN BASIN-ZAYANDEHRUD WATERSHED)

Pages

  23-37

Abstract

RUNOFF estimation is an important part of water resources management of watersheds. Although a lot of physical and statistical methods are available which apply different watershed parameters, the black box artificial neural networks are more suitable methods.In this study, a MULTILAYER PERCEPTRON NETWORK is applied for estimation of daily RUNOFF from daily rainfall of PLASJAN BASIN. At first, daily rainfall series of 3 stations and daily discharge of Plasjan station were normalized and according to AUTOCORRELATION and cross correlation of rainfall-RUNOFF data, 6 variables were selected as input of the network and a 4-hidden-layer network was found to be more valid comparing with other networks.

Cites

References

Cite

APA: Copy

NASRI, MASOUD, MODARES, R., & DASTOURANI, M.T.. (2009). APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR RUNOFF ESTIMATION (CASE STUDY: PLASJAN BASIN-ZAYANDEHRUD WATERSHED). ENVIRONMENTAL BASED TERRITORIAL PLANNING (AMAYESH), 2(5), 23-37. SID. https://sid.ir/paper/130504/en

Vancouver: Copy

NASRI MASOUD, MODARES R., DASTOURANI M.T.. APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR RUNOFF ESTIMATION (CASE STUDY: PLASJAN BASIN-ZAYANDEHRUD WATERSHED). ENVIRONMENTAL BASED TERRITORIAL PLANNING (AMAYESH)[Internet]. 2009;2(5):23-37. Available from: https://sid.ir/paper/130504/en

IEEE: Copy

MASOUD NASRI, R. MODARES, and M.T. DASTOURANI, “APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR RUNOFF ESTIMATION (CASE STUDY: PLASJAN BASIN-ZAYANDEHRUD WATERSHED),” ENVIRONMENTAL BASED TERRITORIAL PLANNING (AMAYESH), vol. 2, no. 5, pp. 23–37, 2009, [Online]. Available: https://sid.ir/paper/130504/en

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