Evaluation of Bayesian Network and Support Vector Machine Models in Estimation of Reference Evapotranspiration (Case Study: Khorramabad)

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Sabzevari Y.; NASROLAHI A.; SHARIFIPOUR M.; SHAHINEJAD B.

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Journal: Iranian Journal of Irrigation and Drainage Year:2020 | Volume:13 | Issue:2 Page(s): 522-534

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Title

Evaluation of Bayesian Network and Support Vector Machine Models in Estimation of Reference Evapotranspiration (Case Study: Khorramabad)

Author(s)

Sabzevari Y. | NASROLAHI A. | SHARIFIPOUR M. | SHAHINEJAD B. | Issue Writer Certificate

Keywords

Bayesian NetworkQ1

KhorramabadQ2

Reference EvapotranspirationQ1

RegressionQ1

Support Vector MachineQ1

Abstract

Around the world, the Penman-Monteithe-FAO model is used as a reference method to estimate Reference Evapotranspiration. This method requires a lot of input data, which in many cases are difficult to access, so it is necessary to replace simpler models with low inputs and good accuracy. Therefore, the purpose of this study was to evaluate the accuracy and capability of Bayesian Network and Support Vector Machine models in estimating Reference Evapotranspiration and comparing it with the Penman-Monteithe-FAO model. For input data, monthly data of Khoramabad synoptic station including: maximum and minimum temperature, maximum and minimum relative humidity, solar radiation and wind speed in period 1990-2016 (420 months) were used. Based on the effect of input parameters on output, six input patterns were determined for modeling. 70% of data were used for training and 30% for model validation. The results showed that pattern number 5 includes: maximum Temperature, wind speed, solar radiation, minimum temperature and minimum relative humidity has the best accuracy in all models. This pattern in test phase, has R2 = 0. 97, RMSE = 0. 93 and R2 = 0. 98, RMSE = 0. 41 respectively in the Bayesian Network and Support Vector Machine with radial basis functions kernel. Comparison of the models showed that the Support Vector Machine has more accuracy with AARE of 0. 0525 and MR of 0. 005.

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APA: Copy

Sabzevari, Y., NASROLAHI, A., SHARIFIPOUR, M., & SHAHINEJAD, B.. (2020). Evaluation of Bayesian Network and Support Vector Machine Models in Estimation of Reference Evapotranspiration (Case Study: Khorramabad). IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE, 13(2 ), 522-534. SID. https://sid.ir/paper/390451/en

Vancouver: Copy

Sabzevari Y., NASROLAHI A., SHARIFIPOUR M., SHAHINEJAD B.. Evaluation of Bayesian Network and Support Vector Machine Models in Estimation of Reference Evapotranspiration (Case Study: Khorramabad). IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE[Internet]. 2020;13(2 ):522-534. Available from: https://sid.ir/paper/390451/en

IEEE: Copy

Y. Sabzevari, A. NASROLAHI, M. SHARIFIPOUR, and B. SHAHINEJAD, “Evaluation of Bayesian Network and Support Vector Machine Models in Estimation of Reference Evapotranspiration (Case Study: Khorramabad),” IRANIAN JOURNAL OF IRRIGATION AND DRAINAGE, vol. 13, no. 2 , pp. 522–534, 2020, [Online]. Available: https://sid.ir/paper/390451/en

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