SOLAR RADIATION DATA AND THEIR INTELLIGENT MODELING BASED ON GAMMA TEST WITH EVALUATION OF CALIBRATED EMPIRICAL EQUATIONS

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GHABAEI SOUGH M.; MOSAEDI A.; DEHGHANI A.A.

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Journal: Journal of Water and Soil Conservation Year:2011 | Volume:18 | Issue:4 Page(s): 185-208

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Title

SOLAR RADIATION DATA AND THEIR INTELLIGENT MODELING BASED ON GAMMA TEST WITH EVALUATION OF CALIBRATED EMPIRICAL EQUATIONS

Author(s)

GHABAEI SOUGH M. | MOSAEDI A. | DEHGHANI A.A. | Issue Writer Certificate

Keywords

SOLAR RADIATIONQ2

QUALITY CONTROLQ2

INTELLIGENT MODELINGQ2

GAMMA TESTQ2

LOCAL LINEAR REGRESSIONQ2

KERMANSHAHQ2

Abstract

SOLAR RADIATION (Rs) is one of the most input parameters in hydrological models and crop growth. Despite its importance for many cases but Rs measurements are not easily available due to the cost, maintenance and calibration requirements of the measuring equipment. Over the past decades, many researchers have developed various equations and none linear models for accurately estimating RS from meteorological parameters. In this study, daily maximum and minimum air temperature, relative humidity, extraterrestrial radiation and actual sunshine duration values from 1992 to 12001 for KERMANSHAH synoptic station, were used as inputs. In first stage, the measured Rs data were investigated to control errors and inconsistencies. After QUALITY CONTROL test appropriate combination and dataset requiring for training nonlinear model and calibrating empirical equations were determined by using GAMMA TEST (GT). Based on GT finding, appropriate combination consists of all input parameters and dataset was needed to training LOCAL LINEAR REGRESSION (LLR), Artificial Neural Network (ANN) and empirical equations equal to1300 datasets. The LLR and ANN with two learning methods (Levenberg-Marquardt (LM) and Conjugate Gradient (SCG)) models based on GAMMA TEST have been implemented and compared with eight locally calibrated empirical RS equations. The comparisons have been based on statistical error criteria, using measured daily RS values. The results indicate that nonlinear model have high accuracy than empirical equation and ANN (LM) with R2 equal 0.9599 and RMSE and MAPE 1.4213 MJ.m-2.d-1 and 6.7616 percent respectively has minimum error.

Cites

PERFORMANCE COMPARISON OF INTEGRATED MODELS OF NN-ARX AND ANFIS WITH GA-GT TO DAILY PAN EVAPORATION ESTIMATION UNDER ARID AND HOT CLIMATE OF BALUCHISTAN

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APA: Copy

GHABAEI SOUGH, M., MOSAEDI, A., & DEHGHANI, A.A.. (2011). SOLAR RADIATION DATA AND THEIR INTELLIGENT MODELING BASED ON GAMMA TEST WITH EVALUATION OF CALIBRATED EMPIRICAL EQUATIONS. JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 18(4), 185-208. SID. https://sid.ir/paper/156259/en

Vancouver: Copy

GHABAEI SOUGH M., MOSAEDI A., DEHGHANI A.A.. SOLAR RADIATION DATA AND THEIR INTELLIGENT MODELING BASED ON GAMMA TEST WITH EVALUATION OF CALIBRATED EMPIRICAL EQUATIONS. JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2011;18(4):185-208. Available from: https://sid.ir/paper/156259/en

IEEE: Copy

M. GHABAEI SOUGH, A. MOSAEDI, and A.A. DEHGHANI, “SOLAR RADIATION DATA AND THEIR INTELLIGENT MODELING BASED ON GAMMA TEST WITH EVALUATION OF CALIBRATED EMPIRICAL EQUATIONS,” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 18, no. 4, pp. 185–208, 2011, [Online]. Available: https://sid.ir/paper/156259/en

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