<abstract xmlns="http://www.w3.org/1999/xhtml">

The study examined the performance of four machine learning algorithms (regression trees, boosted trees, random forests, and artificial neural networks) for estimating evapotranspiration (ETo) based on incomplete meteorological data. Meteorological variables (mean and maximum air temperature, average air humidity, average level of solar radiation, vapor pressure deficit, extraterrestrial solar radiation, and day number of the year) were used as input. The simulation used two calculation scenarios: data with and without average solar radiation. The performance of the different machine learning models was evaluated using the mean square error, root mean square error, coefficient of determination, and slope of regression forced through the origin between the measured and simulated ETo. The results demonstrated that the applied models were able to describe nonlinear relationships between weather parameters and evapotranspiration. The accuracy of evapotranspiration estimation depended on the type of input variables and the machine learning model used. The highest level of evapotranspiration prediction was obtained using the artificial neural networks model. Including solar radiation data in the calculations improved the quality of evapotranspiration prediction in all four models. In the absence of data on the actual solar radiation reaching the Earth's surface, it is advisable to supplement the input data with data on extraterrestrial solar radiation and the day number of the year. Such an approach can be helpful in areas and situations with limited access to meteorological data.
</abstract>

The study examined the performance of four machine learning algorithms (regression trees, boosted trees, random forests, and artificial neural networks) for estimating evapotranspiration (ETo) based on incomplete meteorological data. Meteorological variables (mean and maximum air temperature, average air humidity, average level of solar radiation, vapor pressure deficit, extraterrestrial solar radiation, and day number of the year) were used as input. The simulation used two calculation scenarios: data with and without average solar radiation. The performance of the different machine learning models was evaluated using the mean square error, root mean square error, coefficient of determination, and slope of regression forced through the origin between the measured and simulated ETo. The results demonstrated that the applied models were able to describe nonlinear relationships between weather parameters and evapotranspiration. The accuracy of evapotranspiration estimation depended on the type of input variables and the machine learning model used. The highest level of evapotranspiration prediction was obtained using the artificial neural networks model. Including solar radiation data in the calculations improved the quality of evapotranspiration prediction in all four models. In the absence of data on the actual solar radiation reaching the Earth's surface, it is advisable to supplement the input data with data on extraterrestrial solar radiation and the day number of the year. Such an approach can be helpful in areas and situations with limited access to meteorological data.

Evapotranspiration Estimation Using Machine Learning Methods

Journal of Horticultural Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Evapotranspiration Estimation Using Machine Learning Methods"}

The study examined the performance of four machine learning algorithms (regression trees, boosted trees, random forests, and artificial neural networks) for...

Model	Radiation	R²	Slope	MSE	RMSE
Regression trees	+	0.911	0.911	0.108	0.329
Regression trees	−	0.813	0.813	0.228	0.478
Boosted trees	+	0.942	0.931	0.073	0.269
Boosted trees	−	0.834	0.825	0.205	0.453
Random forests	+	0.952	0.895	0.066	0.256
Random forests	−	0.841	0.799	0.207	0.455
Artificial neural networks	+	0.963	0.947	0.023	0.152
Artificial neural networks	−	0.870	0.843	0.082	0.286

Evapotranspiration Estimation Using Machine Learning Methods

Online veröffentlicht: 29. Dez. 2023

Seitenbereich: 35 - 44

Eingereicht: 01. Sept. 2023

Akzeptiert: 01. Nov. 2023

DOI: https://doi.org/10.2478/johr-2023-0033

Schlüsselwörter
regression trees, boosted trees, random forests, neural networks, weather data, incomplete meteorological data

© 2023 Waldemar Treder et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

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

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

Evapotranspiration Estimation Using Machine Learning Methods

Online veröffentlicht: 29. Dez. 2023

Seitenbereich: 35 - 44

Eingereicht: 01. Sept. 2023

Akzeptiert: 01. Nov. 2023

DOI: https://doi.org/10.2478/johr-2023-0033

Schlüsselwörterregression trees, boosted trees, random forests, neural networks, weather data, incomplete meteorological data

© 2023 Waldemar Treder et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

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

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

Schlüsselwörter
regression trees, boosted trees, random forests, neural networks, weather data, incomplete meteorological data