COMPARISON OF WAVELET NEURAL NETWORK MODELS, SUPPORT VECTOR MACHINE, AND GENE EXPRESSION PROGRAMMING IN ESTIMATING THE AMOUNT OF OXYGEN DISSOLVED IN RIVERS

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SHAHINEJAD B.; DEHAGHANI R.

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Journal: IRAN-WATER RESOURCES RESEARCH Year:2018 | Volume:14 | Issue:3 Page(s): 265-277

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Title

COMPARISON OF WAVELET NEURAL NETWORK MODELS, SUPPORT VECTOR MACHINE, AND GENE EXPRESSION PROGRAMMING IN ESTIMATING THE AMOUNT OF OXYGEN DISSOLVED IN RIVERS

Author(s)

SHAHINEJAD B. | DEHAGHANI R. | Issue Writer Certificate

Keywords

DISSOLVED OXYGENQ1

GENE EXPRESSION PROGRAMMINGQ2

WAVELET NEURAL NETWORKQ1

WATER QUALITYQ2

SUPPORT VECTOR MACHINEQ2

Abstract

DISSOLVED OXYGEN in rivers is one of the most effective parameters in determining WATER QUALITY and its control is one of the most important factors in development of water resources in each region. For this reason, we investigated the performance of WAVELET NEURAL NETWORK models, SUPPORT VECTOR MACHINEs, and GENE EXPRESSION PROGRAMMING for estimating the DISSOLVED OXYGEN in Cumberland River in Tennessee. The indicators of the Cumberland River, including DO, flow rate and temperature during 10-year period (2006-2016), were simulated in monthly time scale. Also for evaluation and performance of the models, the correlation coefficient, root mean square error and mean absolute magnitude error were applied. The results showed that integrated input structures into models in all three models offer better performance than other structures. Also, the results of the evaluation criteria showed that the WAVELET NEURAL NETWORK model showed the highest correlation coefficient (0. 960), lowest root mean square error (0. 668), and the lowest mean error magnitude (519. 0). In general, the results showed that due to the high ability of the WAVELET NEURAL NETWORK and the elimination of time series noise in the estimation of river WATER QUALITY parameters, this model could be a suitable and rapid solution for water resource quality management.

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APA: Copy

SHAHINEJAD, B., & DEHAGHANI, R.. (2018). COMPARISON OF WAVELET NEURAL NETWORK MODELS, SUPPORT VECTOR MACHINE, AND GENE EXPRESSION PROGRAMMING IN ESTIMATING THE AMOUNT OF OXYGEN DISSOLVED IN RIVERS. IRAN-WATER RESOURCES RESEARCH, 14(3 ), 265-277. SID. https://sid.ir/paper/100429/en

Vancouver: Copy

SHAHINEJAD B., DEHAGHANI R.. COMPARISON OF WAVELET NEURAL NETWORK MODELS, SUPPORT VECTOR MACHINE, AND GENE EXPRESSION PROGRAMMING IN ESTIMATING THE AMOUNT OF OXYGEN DISSOLVED IN RIVERS. IRAN-WATER RESOURCES RESEARCH[Internet]. 2018;14(3 ):265-277. Available from: https://sid.ir/paper/100429/en

IEEE: Copy

B. SHAHINEJAD, and R. DEHAGHANI, “COMPARISON OF WAVELET NEURAL NETWORK MODELS, SUPPORT VECTOR MACHINE, AND GENE EXPRESSION PROGRAMMING IN ESTIMATING THE AMOUNT OF OXYGEN DISSOLVED IN RIVERS,” IRAN-WATER RESOURCES RESEARCH, vol. 14, no. 3 , pp. 265–277, 2018, [Online]. Available: https://sid.ir/paper/100429/en

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