Sensitivity analysis of meteorological data in estimating reference evapotranspiration with the minimum data using wavelet-neuro-fuzzy, ANN and ANFIS models

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KARIMIPOUR AHMADREZA; Banitalebi Golnoosh

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Journal: JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION Year:2020 | Volume:9 | Issue:3 Page(s): 47-71

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Title

Sensitivity analysis of meteorological data in estimating reference evapotranspiration with the minimum data using wavelet-neuro-fuzzy, ANN and ANFIS models

Author(s)

KARIMIPOUR AHMADREZA | Banitalebi Golnoosh | Issue Writer Certificate

Keywords

ANFISQ1

Sensitivity analysisQ2

FAOQ1

PenmanQ1

MonteithQ1

FuzzyQ1

neuroQ1

wavelet modelQ1

Abstract

The aim of this study was to estimate the ET0 in a moderately cold semi-humid climate in a 22year statistical period by applying a wavelet-neuro-Fuzzy model with a minimum number of input parameters. The results were compared with the ANN and ANFIS models to evaluate the performance of the wavelet-neuro-Fuzzy model. The Sensitivity analysis of the input parameters was done in three ways: the Hill method, coefficient of determination, and StatSoft methods. Sensitivity analysis showed that temperature (T) (minimum, maximum and average of daily air temperature), Rs, Ra, mean daily wind speed at 2 meters (U2) and Rn (net radiation) were an effective parameter, and different combinations of these input parameters can lead to more accurate estimate of ET0. Based on the results of the Sensitivity analysis, six combinations with these parameters were selected, and temperature in all of these compounds was used as an input variable. Using three input parameters; Tmin, Tmax, and Rs and sym8 wavelet, the wavelet-neural-Fuzzy model had a better performance than the neural network model. The results also showed that the estimated ET0 value with three inputs parameters of maximum and minimum temperature and solar radiation using the Fuzzy-neural-wavelet model was more accurate than the neural network. Based on the coefficient of determination and the amount of calculated error for the artificial neural network and the ANFIS, use of the combination of 7 input parameters (Ra, Rn, Rs, U2, Tmean, Tmin, and Tmax) (MBEANN=0. 003, MBEANFIS=0. 007, R2ANN=0. 99, R2ANFIS=0. 99) and four meteorological input parameters (Ra, U2, Tmean, and Tmax) (MBEANN=0. 07, MBEANFIS=0. 05, R2ANN=0. 93, R2ANFIS=0. 98) led to more accurate estimates of ET0 in comparison to the FAO Penman-Monteith method. The results also showed that the highest amount of coefficient of determination and the lowest error value among the different wavelets used in the Fuzzy-neuro-wavelet model was for the seven (R2=0. 87, MBE=0. 02) and three input parameters (Tmax, Tmin, Rs) (R2=0. 71, MBE=0. 03), respectively. The results also indicated that, concerning the error criteria (EF, RMSE, NRMSE, MBE and MAE) and the coefficient of determination, the best model for estimation of ET0 was Hargriverz-M4, Tylor-Priestly, Meyer, and IRMAK. Based on the EF and NRMSE, four methods including Hargriverz-M4 (EF=0. 92, NRMSE=0. 11), Tylor-Priestly (EF=0. 88, NRMSE=0. 12), Meyer (EF=0. 73, NRMSE=0. 03) and IRMAK1 (EF=0. 67, NRMSE=0. 25) in long-time period, in according to EF, Hargriverz-M4 (0. 92) and in relation to NRMSE, Meyer had the better performance than the other methods.

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APA: Copy

KARIMIPOUR, AHMADREZA, & Banitalebi, Golnoosh. (2020). Sensitivity analysis of meteorological data in estimating reference evapotranspiration with the minimum data using wavelet-neuro-fuzzy, ANN and ANFIS models. JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION, 9(3 ), 47-71. SID. https://sid.ir/paper/414319/en

Vancouver: Copy

KARIMIPOUR AHMADREZA, Banitalebi Golnoosh. Sensitivity analysis of meteorological data in estimating reference evapotranspiration with the minimum data using wavelet-neuro-fuzzy, ANN and ANFIS models. JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION[Internet]. 2020;9(3 ):47-71. Available from: https://sid.ir/paper/414319/en

IEEE: Copy

AHMADREZA KARIMIPOUR, and Golnoosh Banitalebi, “Sensitivity analysis of meteorological data in estimating reference evapotranspiration with the minimum data using wavelet-neuro-fuzzy, ANN and ANFIS models,” JOURNAL OF WATER AND SOIL RESOURCES CONSERVATION, vol. 9, no. 3 , pp. 47–71, 2020, [Online]. Available: https://sid.ir/paper/414319/en

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