MONTHLY FORECAST OF POTENTIAL EVAPOTRANSPIRATION MODELS USING SUPPORT VECTOR MACHINE (SVM), GENETIC PROGRAMMING AND NEURAL - FUZZY INFERENCE SYSTEM

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MOHAMADREZAPOUR OMOLBANIN

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Journal: Journal of Irrigation and Water Engineering Year:2017 | Volume:7 | Issue:27 Page(s): 135-150

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Title

MONTHLY FORECAST OF POTENTIAL EVAPOTRANSPIRATION MODELS USING SUPPORT VECTOR MACHINE (SVM), GENETIC PROGRAMMING AND NEURAL - FUZZY INFERENCE SYSTEM

Author(s)

MOHAMADREZAPOUR OMOLBANIN | Issue Writer Certificate

Keywords

CLIMATE PARAMETERSQ1

MODELINGQ2

PENMAN – MONTEITH (P-M)Q1

SISTAN AND BALUCHISTANQ1

Abstract

Despite the importance of evapotranspiration in the planning and management of water resources, its dependence on climatic factors on the one hand and influence of each of these components on the other hand has made it difficult to estimate evapotranspiration. Therefore, in this study, attempts to explore the possibility of predicting these important component in Sistan and Baluchestan using meta-heuristic models such as neuro-fuzzy inference system, GEP and SVM. In this regard, according to the FAO Penman-Monteith equation, the monthly potential evapotranspiration in four synoptic stations- Zahedan, Zabol, Iranshahr, and Chabahar- was calculated using the monthly weather data. These values as a reference to compare the results of the Neuro-fuzzy inference models, genetic programming, and SVM methods were studied. The five models applied in this study were: Model 1 includes input of average air temperature, shiny hours and relative humidity in the same month. Model 2 includes average air temperature, relative humidity, and wind speed in the same month. Model 3 includes average air temperature, relative humidity, and wind speed in the same month. Model 4 includes average air temperature, relative humidity, wind speed, and average shiny hours in the same month, and model 5 includes average air temperature, relative humidity, wind speed, and shiny hours in the same month and the earlier month. The results of different models were compared based on the statistical coefficient of determination and root mean square error. These findings show that in the neuro-fuzzy model, the models 2 (r2=0.945), 3 (r2=0.982 (, 4 (r2=0.26), and 5 (r2=0.423), respectively in Zahedan, Zabul and Chabahar, and Iranshahr Chabahar stations own greater accuracy. Analysis of results in the gene- expression planning model also indicates that in the test section, the model no.4, with the coefficients of 0.974, 0.9811, 0.982, and 0.815, respectively for the stations of Zahedan, Zabol, Iranshahr, and Chabahar, has higher accuracy. Likewise, in the SVM model, due to the coefficients of determination, 0.997, 0.998, 0.998, and 0.979, respectively in the stations of Zahedan, Zabol, Iranshahr, and Chabahar, the model 5 had the highest accuracy. Comparison of 3 models in this study also showed that in all stations, the Support Vector Machine, the programming model of gene expression, and the neuro-fuzzy model were paced in the first, the second, and the third levels of importance for estimating the monthly potential evapotranspiration.

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APA: Copy

MOHAMADREZAPOUR, OMOLBANIN. (2017). MONTHLY FORECAST OF POTENTIAL EVAPOTRANSPIRATION MODELS USING SUPPORT VECTOR MACHINE (SVM), GENETIC PROGRAMMING AND NEURAL - FUZZY INFERENCE SYSTEM. IRANIAN OF IRRIGATION & WATER ENGINEERING, 7(27), 135-150. SID. https://sid.ir/paper/247106/en

Vancouver: Copy

MOHAMADREZAPOUR OMOLBANIN. MONTHLY FORECAST OF POTENTIAL EVAPOTRANSPIRATION MODELS USING SUPPORT VECTOR MACHINE (SVM), GENETIC PROGRAMMING AND NEURAL - FUZZY INFERENCE SYSTEM. IRANIAN OF IRRIGATION & WATER ENGINEERING[Internet]. 2017;7(27):135-150. Available from: https://sid.ir/paper/247106/en

IEEE: Copy

OMOLBANIN MOHAMADREZAPOUR, “MONTHLY FORECAST OF POTENTIAL EVAPOTRANSPIRATION MODELS USING SUPPORT VECTOR MACHINE (SVM), GENETIC PROGRAMMING AND NEURAL - FUZZY INFERENCE SYSTEM,” IRANIAN OF IRRIGATION & WATER ENGINEERING, vol. 7, no. 27, pp. 135–150, 2017, [Online]. Available: https://sid.ir/paper/247106/en

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