RAINFALL-RUNOFF MODELING USING HYBRID INTELLIGENT MODELS (TECHNICAL NOTE)

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GHORBANI M.A.; AZANI A.; MAHMOUDI VANOLYA S.

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Paper Information

Journal: IRAN-WATER RESOURCES RESEARCH Year:2015 | Volume:11 | Issue:2 (33) Page(s): 146-150

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Title

RAINFALL-RUNOFF MODELING USING HYBRID INTELLIGENT MODELS (TECHNICAL NOTE)

Author(s)

GHORBANI M.A. | AZANI A. | MAHMOUDI VANOLYA S. | Issue Writer Certificate

Keywords

RAINFALL-RUNOFFQ2

WAVELET TRANSFORMQ2

ARTIFICIAL NEURAL NETWORKSQ2

SUPPORT VECTOR MACHINESQ2

HYBRIDQ2

Abstract

RAINFALL-RUNOFF is considered one of the most important processes in water resources studies. In this study, to simulate the daily RAINFALL-RUNOFF process of Balikhluchay Basin, four HYBRID models of Support Vector Machine, ARTIFICIAL NEURAL NETWORKS, Wavelet-Support Vector Machine, and Wavelet-Neural Networks have been applied and compared. Daily RAINFALL-RUNOFF data for the period of 2000 to 2008, have been used for training and testing the models. In general, the results indicated acceptable accuracy of all the models. In terms of priority, the HYBRID model of Wavelet-Neural Network with the highest accuracy and lowest errors was in the first rank and the HYBRID models of Wavelet-Support Vector Machines, ARTIFICIAL NEURAL NETWORKS and SUPPORT VECTOR MACHINES, were in next priorities.

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APA: Copy

GHORBANI, M.A., AZANI, A., & MAHMOUDI VANOLYA, S.. (2015). RAINFALL-RUNOFF MODELING USING HYBRID INTELLIGENT MODELS (TECHNICAL NOTE). IRAN-WATER RESOURCES RESEARCH, 11(2 (33)), 146-150. SID. https://sid.ir/paper/100028/en

Vancouver: Copy

GHORBANI M.A., AZANI A., MAHMOUDI VANOLYA S.. RAINFALL-RUNOFF MODELING USING HYBRID INTELLIGENT MODELS (TECHNICAL NOTE). IRAN-WATER RESOURCES RESEARCH[Internet]. 2015;11(2 (33)):146-150. Available from: https://sid.ir/paper/100028/en

IEEE: Copy

M.A. GHORBANI, A. AZANI, and S. MAHMOUDI VANOLYA, “RAINFALL-RUNOFF MODELING USING HYBRID INTELLIGENT MODELS (TECHNICAL NOTE),” IRAN-WATER RESOURCES RESEARCH, vol. 11, no. 2 (33), pp. 146–150, 2015, [Online]. Available: https://sid.ir/paper/100028/en

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