<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...

Evapotranspiration Estimation Using Machine Learning Methods

Data publikacji: 29 gru 2023

Zakres stron: 35 - 44

Otrzymano: 01 wrz 2023

Przyjęty: 01 lis 2023

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

Słowa kluczowe
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.

Evapotranspiration Estimation Using Machine Learning Methods

Data publikacji: 29 gru 2023

Zakres stron: 35 - 44

Otrzymano: 01 wrz 2023

Przyjęty: 01 lis 2023

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

Słowa kluczoweregression 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.

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