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Evapotranspiration Estimation Using Machine Learning Methods

Waldemar Treder

Waldemar Treder

The National Institute of Horticultural ResearchSkierniewice, Poland
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Treder, Waldemar
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Krzysztof Klamkowski

Krzysztof Klamkowski

The National Institute of Horticultural ResearchSkierniewice, Poland
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Klamkowski, Krzysztof
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Katarzyna Wójcik

Katarzyna Wójcik

The National Institute of Horticultural ResearchSkierniewice, Poland
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Wójcik, Katarzyna
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Anna Tryngiel-Gać

Anna Tryngiel-Gać

The National Institute of Horticultural ResearchSkierniewice, Poland
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Tryngiel-Gać, Anna
  
29 déc. 2023
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Publié en ligne: 29 déc. 2023
Pages: 35 - 44
Reçu: 01 sept. 2023
Accepté: 01 nov. 2023
DOI: https://doi.org/10.2478/johr-2023-0033
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© 2023 Waldemar Treder et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Relationship between evapotranspiration (ETo) and important weather parameters (Skierniewice 2009–2022)
Relationship between evapotranspiration (ETo) and important weather parameters (Skierniewice 2009–2022)

Figure 2.

The importance of variables when creating regression treesSR – average solar radiation level, VPD – vapor pressure deficit, Tmax – maximum temperature, Tavg – average temperature, RH – relative humidity, Ra – extraterrestrial solar radiation, #D – day number of the year
The importance of variables when creating regression treesSR – average solar radiation level, VPD – vapor pressure deficit, Tmax – maximum temperature, Tavg – average temperature, RH – relative humidity, Ra – extraterrestrial solar radiation, #D – day number of the year

Figure 3.

The importance of variables when creating boosted treesNote: see Figure 2
The importance of variables when creating boosted treesNote: see Figure 2

Figure 4.

The importance of variables when creating random forestsNote: see Figure 2
The importance of variables when creating random forestsNote: see Figure 2

Figure 5.

Net changes in extraterrestrial solar radiation (Ra) and the average level of solar radiation (SR) during the vegetation period (Skierniewice 2009–2022)
Net changes in extraterrestrial solar radiation (Ra) and the average level of solar radiation (SR) during the vegetation period (Skierniewice 2009–2022)

Pearson correlation coefficients between daily evapotranspiration (ETo) and meteorological data

SR Tavg Tmax RH U2 Ra VPD #D
ETo 0.94 0.66 0.72 −0.69 0.05 0.62 0.82 −0.41

Statistical analysis of the performance of the RT, BRT, RF, and ANN models in estimating daily ETo with two different meteorological input datasets

Model Radiation R2 Slope MSE RMSE
Regression trees + 0.911 0.911 0.108 0.329
− 0.813 0.813 0.228 0.478
Boosted trees + 0.942 0.931 0.073 0.269
− 0.834 0.825 0.205 0.453
Random forests + 0.952 0.895 0.066 0.256
− 0.841 0.799 0.207 0.455
Artificial neural networks + 0.963 0.947 0.023 0.152
− 0.870 0.843 0.082 0.286
Langue:
Anglais
Périodicité:
2 fois par an
Sujets de la revue:
Sciences de la vie, Biotechnologie, Sciences végétales, Écologie, Sciences de la vie, autres
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