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. 2020 May 13;22(5):547.
doi: 10.3390/e22050547.

Reference Evapotranspiration Modeling Using New Heuristic Methods

Rana Muhammad Adnan  1 , Zhihuan Chen  2 , Xiaohui Yuan  3   4 , Ozgur Kisi  5 , Ahmed El-Shafie  6 , Alban Kuriqi  7 , Misbah Ikram  1   8
Affiliations

Reference Evapotranspiration Modeling Using New Heuristic Methods

Rana Muhammad Adnan et al. Entropy (Basel). .

Abstract

The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods are compared with the M5 model tree (M5RT) approach. Previous values of temperature data and extraterrestrial radiation information obtained from three stations, in China, are used as inputs to the models. The estimation exactness of the models is measured by three statistics: root mean square error, mean absolute error, and determination coefficient. According to the results, the temperature or extraterrestrial radiation-based LSSVR-GSA models perform superiorly to the DENFIS and M5RT models in terms of estimating monthly ETo. However, in some cases, a slight difference was found between the LSSVR-GSA and DENFIS methods. The results indicate that better prediction accuracy may be obtained using only extraterrestrial radiation information for all three methods. The prediction accuracy of the models is not generally improved by including periodicity information in the inputs. Using optimum air temperature and extraterrestrial radiation inputs together generally does not increase the accuracy of the applied methods in the estimation of monthly ETo.

Keywords: dynamic evolving neural-fuzzy inference system; gravitational search algorithm; least square support vector regression; reference evapotranspiration; temperature input.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Study area.
Figure 2
Figure 2
LSSVR model for evapotranspiration (ETo) modeling.
Figure 3
Figure 3
Schematic view of M5 model tree (a) structure and (b) splitting data space into sub-regions.
Figure 4
Figure 4
(a) Time variation graphs of the FAO 56 PM and estimated ETo by LSSVR-GSA, DENFIS, and M5RT in the test period of Station 56004 using optimal T inputs. (b) Time variation graphs of the FAO 56 PM and estimated ETo by LSSVR-GSA, DENFIS, and M5RT in the test period of Station 56004 using optimal Ra inputs.
Figure 4
Figure 4
(a) Time variation graphs of the FAO 56 PM and estimated ETo by LSSVR-GSA, DENFIS, and M5RT in the test period of Station 56004 using optimal T inputs. (b) Time variation graphs of the FAO 56 PM and estimated ETo by LSSVR-GSA, DENFIS, and M5RT in the test period of Station 56004 using optimal Ra inputs.
Figure 5
Figure 5
Time scatterplots of the observed and estimated ETo (by LSSVR-GSA, DENFIS, and M5RT) in the test period of Station 56029 using optimal (a) T and (b) Ra inputs.
Figure 6
Figure 6
(a) Time variation graphs of the FAO 56 PM and estimated ETo (by LSSVR-GSA, DENFIS, and M5RT) in the test period of Station 56021 using optimal T inputs. (b) Time variation graphs of the FAO 56 PM and estimated ETo by LSSVR-GSA, DENFIS, and M5RT in the test period of Station 56021 using optimal Ra inputs.
Figure 7
Figure 7
Time scatterplots of the observed and estimated ETo (by LSSVR-GSA, DENFIS, and M5RT) in the test period of Station 56021 using optimal (a) T and (b) Ra inputs.
Figure 8
Figure 8
(a)Time variation graphs of the FAO 56 PM and estimated ETo (by LSSVR-GSA, DENFIS, and M5RT) in the test period of Station 56029 using optimal T inputs. (b) Time variation graphs of the FAO 56 PM and estimated ETo by LSSVR-GSA, DENFIS, and M5RT in the test period of Station 56029 using optimal Ra inputs.
Figure 8
Figure 8
(a)Time variation graphs of the FAO 56 PM and estimated ETo (by LSSVR-GSA, DENFIS, and M5RT) in the test period of Station 56029 using optimal T inputs. (b) Time variation graphs of the FAO 56 PM and estimated ETo by LSSVR-GSA, DENFIS, and M5RT in the test period of Station 56029 using optimal Ra inputs.
Figure 9
Figure 9
Time scatterplots of the observed and estimated ETo (by LSSVR-GSA, DENFIS, and M5RT) in the test period of Station 56021 using optimal (a) T and (b) Ra inputs.
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References

    1. Gavili S., Sanikhani H., Kisi O., Mahmoudi M.H. Evaluation of several soft computing methods in monthly evapotranspiration modelling. Meteorol. Appl. 2018;25:128–138. doi: 10.1002/met.1676. - DOI
    1. Sanikhani H., Kisi O., Maroufpoor E., Yaseen Z.M. Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: Application of different modeling scenarios. Theor. Appl. Climatol. 2019;135:449–462. doi: 10.1007/s00704-018-2390-z. - DOI
    1. Almorox J., Senatore A., Quej V.H., Mendicino G. Worldwide assessment of the Penman–Monteith temperature approach for the estimation of monthly reference evapotranspiration. Theor. Appl. Climatol. 2018;131:693–703. doi: 10.1007/s00704-016-1996-2. - DOI
    1. Kumar M., Raghuwanshi N.S., Singh R., Wallender W.W., Pruitt W.O. Estimating evapotranspiration using artificial neural network. J. Irrig. Drain. Eng. 2002;128:224–233. doi: 10.1061/(ASCE)0733-9437(2002)128:4(224). - DOI
    1. Tie Q., Hu H., Tian F., Holbrook N.M. Comparing different methods for determining forest evapotranspiration and its components at multiple temporal scales. Sci. Total Environ. 2018;633:12–29. doi: 10.1016/j.scitotenv.2018.03.082. - DOI - PubMed

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