EVALUATION OF ARTIFICIAL INTELLIGENT AND EMPIRICAL MODELS IN ESTIMATION OF ANNUAL RUNOFF

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ZARE ABYANEH H.; BAYAT VARKESHI M.

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Journal: Journal of Water and Soil Year:2011 | Volume:25 | Issue:2 Page(s): 365-379

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Information Journal Paper

Title

EVALUATION OF ARTIFICIAL INTELLIGENT AND EMPIRICAL MODELS IN ESTIMATION OF ANNUAL RUNOFF

Author(s)

ZARE ABYANEH H. | BAYAT VARKESHI M. | Issue Writer Certificate

Keywords

ESTIMATION OF RUNOFFQ2

EMPIRICAL METHODQ2

ANNQ2

CANFISQ2

ZAYANDEH ROOD BASINQ2

Abstract

From Longley, the various equations for determining the runoff to water management are presented by the researchers that are widely used in hydrologic sciences. In this study by using observational data, was evaluated empirical, artificial neural network (ANN) and ca-active neuro-fuzzy inference system (CANFIS) models in ESTIMATION OF RUNOFF. For this purpose, by using climatic and physiographic information in three stations of Pole Zamankhan, Ghale Shahrokh and Sade Zayandeh Rood, runoff values were estimated from empirical models and intelligent models were compared to ANNual runoff values. Input parameters include rain, mean temperature, mininmum temperature and maximum temperature. The results showed that the artificial intelligent models had good accuracy in estimating runoff. Among the EMPIRICAL METHODs, method of Di Souza was appropriate.Comparison statistical parameters between methods was showed that mean percent error (MPE) in ANN, CANFIS and EMPIRICAL METHOD was 7, 12 and 43 percent respectively that confirmed differences of between the methods is significant. Also, CANFIS model did not artificial improve ANN results. The results showed, with reduction of input variables from 4 parameters to one parameter of precipitation, modeling error reaches its maximum value (from MPE=7% to MPE=16%). Versus, the optimal structure of ANN had less sensitivity to remove the mean air temperature parameter (from MPE=7% to MPE=10%). Therefore, according to empirical models required information limitations and high accuracy of artificial intelligent models, intelligent models application is recommended.

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APA: Copy

ZARE ABYANEH, H., & BAYAT VARKESHI, M.. (2011). EVALUATION OF ARTIFICIAL INTELLIGENT AND EMPIRICAL MODELS IN ESTIMATION OF ANNUAL RUNOFF. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), 25(2), 365-379. SID. https://sid.ir/paper/141723/en

Vancouver: Copy

ZARE ABYANEH H., BAYAT VARKESHI M.. EVALUATION OF ARTIFICIAL INTELLIGENT AND EMPIRICAL MODELS IN ESTIMATION OF ANNUAL RUNOFF. JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY)[Internet]. 2011;25(2):365-379. Available from: https://sid.ir/paper/141723/en

IEEE: Copy

H. ZARE ABYANEH, and M. BAYAT VARKESHI, “EVALUATION OF ARTIFICIAL INTELLIGENT AND EMPIRICAL MODELS IN ESTIMATION OF ANNUAL RUNOFF,” JOURNAL OF WATER AND SOIL (AGRICULTURAL SCIENCES AND TECHNOLOGY), vol. 25, no. 2, pp. 365–379, 2011, [Online]. Available: https://sid.ir/paper/141723/en

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