EVALUATING THE EFFECTS OF DIFFERENT INPUT SIGNALS ON EFFICIENCY COEFFICIENTS OF ARTIFICIAL NEURAL NETWORK MODELS FOR INTELLIGENT ESTIMATION OF FLOOD HYDROGRAPHS

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PAHLAVANI H.; BAHREMAND A.; DEHGHANI A.A.

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Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING Year:2010 | Volume:4 | Issue:11 Page(s): 45-48

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Title

EVALUATING THE EFFECTS OF DIFFERENT INPUT SIGNALS ON EFFICIENCY COEFFICIENTS OF ARTIFICIAL NEURAL NETWORK MODELS FOR INTELLIGENT ESTIMATION OF FLOOD HYDROGRAPHS

Author(s)

PAHLAVANI H. | BAHREMAND A. | DEHGHANI A.A. | Issue Writer Certificate

Keywords

FLOOD HYDROGRAPHQ2

ARTIFICIAL NEURAL NETWORKQ2

SHIRINDARREH RESERVOIR DAM BASINQ3

LEARNING ALGORITHMS AND TRANSFER FUNCTIONQ3

Abstract

The estimation of FLOOD HYDROGRAPH characteristics in natural rivers is of hydrologists interests. In this paper, the ability of neural networks for estimation of FLOOD HYDROGRAPH to Shirindareh reservoir dam in Khorasan province is evaluated. Therefore, all FLOOD HYDROGRAPH events of hydrometery station in upstream were collected and normalized. It is notable that the flood hydrometery was estimated 2, 3, 4 and 5 hours earlier using the flood discharges at 2, 3, 4 and 5 previous hours as model inputs respectively. In each pattern, four signals were selected for considering the effect of number of inputs for estimation of FLOOD HYDROGRAPH.The results show that by increasing the estimation lag time, the accuracy of results decrease and in given lag time, by increasing the number of input, the accuracy of results increase. The results show that the amount of efficiency coefficients, which is the representation of goodness of FLOOD HYDROGRAPH modeling, is increased from 0.79 for signal one to 0.91 for signal four.

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References

EVALUATION OF THE APPLICATION OF ARTIFICIAL INTELLIGENCE MODEL FOR SIMULATION AND REAL – TIME PREDICTION OF FLOOD FLOW

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APA: Copy

PAHLAVANI, H., BAHREMAND, A., & DEHGHANI, A.A.. (2010). EVALUATING THE EFFECTS OF DIFFERENT INPUT SIGNALS ON EFFICIENCY COEFFICIENTS OF ARTIFICIAL NEURAL NETWORK MODELS FOR INTELLIGENT ESTIMATION OF FLOOD HYDROGRAPHS. IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING, 4(11), 45-48. SID. https://sid.ir/paper/134882/en

Vancouver: Copy

PAHLAVANI H., BAHREMAND A., DEHGHANI A.A.. EVALUATING THE EFFECTS OF DIFFERENT INPUT SIGNALS ON EFFICIENCY COEFFICIENTS OF ARTIFICIAL NEURAL NETWORK MODELS FOR INTELLIGENT ESTIMATION OF FLOOD HYDROGRAPHS. IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING[Internet]. 2010;4(11):45-48. Available from: https://sid.ir/paper/134882/en

IEEE: Copy

H. PAHLAVANI, A. BAHREMAND, and A.A. DEHGHANI, “EVALUATING THE EFFECTS OF DIFFERENT INPUT SIGNALS ON EFFICIENCY COEFFICIENTS OF ARTIFICIAL NEURAL NETWORK MODELS FOR INTELLIGENT ESTIMATION OF FLOOD HYDROGRAPHS,” IRANIAN JOURNAL OF WATERSHED MANAGEMENT SCIENCE AND ENGINEERING, vol. 4, no. 11, pp. 45–48, 2010, [Online]. Available: https://sid.ir/paper/134882/en

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