Estimasi Evapotranspirasi Potensial Menggunakan Algoritma Random Forest di Daerah Irigasi Tungkub, Bali

Authors

  • Luh Made Putri Apriliani Program Studi Teknik Pertanian dan Biosistem, Fakultas Teknologi Pertanian, Universitas Udayana, Badung, Bali, Indonesia
  • Ni Nyoman Sulastri Program Studi Teknik Pertanian dan Biosistem, Fakultas Teknologi Pertanian, Universitas Udayana, Badung, Bali, Indonesia
  • I Wayan Widia Program Studi Teknik Pertanian dan Biosistem, Fakultas Teknologi Pertanian, Universitas Udayana, Badung, Bali, Indonesia
  • I Putu Gede Budisanjaya Program Studi Teknik Pertanian dan Biosistem, Fakultas Teknologi Pertanian, Universitas Udayana, Badung, Bali, Indonesia

DOI:

https://doi.org/10.24843/j.beta.2025.v13.i01.p16

Keywords:

machine learning, potential evapotranspiration, random forest, weather variabel

Abstract

The estimation of Potential Evapotranspiration (ETp) is crucial for water distribution planning and
cropping patterns. Generally, ETp calculation is obtained from empirical models such as the PenmanMonteith (PM) model recommended by the Food and Agriculture Organization (FAO). However,
implementing this model requires numerous weather variables and adequate weather data availability. This
research aims to develop an ETp estimation model using the Random Forest (RF) algorithm The weather
variables used in this research as inputs for ETp modeling are solar radiation (Rs); air temperature (T); air
humidity (RH); and a combination of Rs and T. Weather variable data were obtained from an automatic
weather station (AWS) in the Tungkub Irrigation Area, Bali. The research results indicate that the weather
variable Rs is the best estimation model input, while the weather variable RH is the weakest. In model
calibration, three evaluation metrics were used to assess model performance, R2, MSE, and RMSE.
Meanwhile, for model validation, three techniques were employed, prediction error plot, residuals plot,
and k-fold cross-validation. The research results indicate that the average ETp estimation value with the
scenario of input Rs using the RF algorithm in the Tungkub Irrigation Area is 0,14 mm/hour (R2 = 1,00,
MSE = 0,00, RMSE = 0,01). Meanwhile, the average ETp PM value is 0,15 mm/hour. The scenario of
input Rs using the RF algorithm shows estimation values close to the PM ETp value.

References

Abedi-Koupai, J., Dorafshan, M. M., Javadi, A., & Ostad-Ali-Askari, K. (2022). Estimating potential reference evapotranspiration using time series models (case study: synoptic station of Tabriz in northwestern Iran). Applied Water Science, 12(9), 1–8. https://doi.org/10.1007/s13201-022-01736-x

Adib, A., Kalantarzadeh, S. S. O., Shoushtari, M. M., Lotfirad, M., Liaghat, A., & Oulapour, M. (2023). Sensitive analysis of meteorological data and selecting appropriate machine learning model for estimation of reference evapotranspiration. Applied Water Science, 13(3), 1–17. https://doi.org/10.1007/s13201-023-01895-5

Ajjur, S. B., & Al-Ghamdi, S. G. (2021). Evapotranspiration and water availability response to climate change in the Middle East and North Africa. Climatic Change, 166(3–4), 1–18. https://doi.org/10.1007/s10584-021-03122-z

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop Evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and Drainage Paper 56. https://www.fao.org/3/X0490E/x0490e00.htm#Contents

Arif, C., Setiawan, B. I., & Sofiyuddin, H. A. (2020). Analisis evapotranspirasi potensial pada berbagai model empiris dan jaringan syaraf tiruan dengan data cuaca terbatas. Jurnal Irigasi, 15(2), 71–84. https://doi.org/10.31028/ji.v15.i2.71-84

Bayram, S., & Çıtakoğlu, H. (2023). Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods. Environmental Monitoring and Assessment, 195(1). https://doi.org/10.1007/s10661-022-10662-z

Bengfort, B., & Bilbro, R. (2019). Yellowbrick: Visualizing the Scikit-Learn Model Selection Process. Journal of Open Source Software, 4(35), 1075.

https://doi.org/10.21105/joss.01075

Dinpashoh, Y., Jahanbakhsh-Asl, S., Rasouli, A. A.,149 Foroughi, M., & Singh, V. P. (2019). Impact of climate change on potential evapotranspiration (case study: west and NW of Iran). Theoretical and Applied Climatology, 136(1–2), 185–201. https://doi.org/10.1007/s00704-018-2462-0

Dou, X., & Yang, Y. (2018). Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems. Computers and Electronics in Agriculture, 148(February), 95–106. https://doi.org/10.1016/j.compag.2018.03.010

Fereres, E., & Villalobos, F. J. (2016). Agriculture and Agricultural Systems BT - Principles of Agronomy for Sustainable Agriculture (F. J.

Villalobos & E. Fereres (eds.); pp. 1–12). Springer International Publishing. https://doi.org/10.1007/978-3-319-46116-8_1

Gad, H. E. (2010). the Effect of Solar Radiation on Animals. Medical Journal of Australia, 2(23), 795–795. https://doi.org/10.5694/j.1326-

5377.1936.tb103240.x

Gao, Z., He, J., Dong, K., & Li, X. (2017). Trends in reference evapotranspiration and their causative factors in the West Liao River basin, China.

Agricultural and Forest Meteorology, 232, 106–117. https://doi.org/10.1016/j.agrformet.2016.08.006

Ge, J., Zhao, L., Yu, Z., Liu, H., Zhang, L., Gong, X., & Sun, H. (2022). Prediction of Greenhouse Tomato Crop Evapotranspiration Using XGBoost Machine Learning Model. Plants, 11(15), 1–17. https://doi.org/10.3390/plants11151923

Granata, F. (2019). Evapotranspiration evaluation models based on machine learning algorithms—A comparative study. Agricultural Water Management, 217(March), 303–315. https://doi.org/10.1016/j.agwat.2019.03.015

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2011). Multivariate Data Analysis (Fifth Edit). Horvitz, E., & Mulligan, D. (2015). Machine

learning: Trends,perspectives, and prospects. Science, 349(6245), 253–255.

Iannelli, M., Rahman, M. R., Choi, N., & Wang, L. (2020). Applying machine learning to end-toend slice SLA decomposition. Proceedings of the 2020 IEEE Conference on Network Softwarization: Bridging the Gap Between AI and Network Softwarization, NetSoft 2020, 92–99. https://doi.org/10.1109/NetSoft48620.2020.9165317

Jha, S. B., Babiceanu, R. F., Pandey, V., & Jha, R. K. (2020). Housing Market Prediction Problem using Different Machine Learning Algorithms:

A Case Study. http://arxiv.org/abs/2006.10092

Liu, J., Yu, K., Li, P., Jia, L., Zhang, X., Yang, Z., & Zhao, Y. (2022). Estimation of Potential Evapotranspiration in the Yellow River Basin Using Machine Learning Models. Atmosphere, 13(9). https://doi.org/10.3390/atmos13091467

Lotfi, M., Kamali, G. A., Meshkatee, A. H., & Varshavian, V. (2020). Study on the impact of climate change on evapotranspiration in west of Iran. Arabian Journal of Geosciences, 13(15). https://doi.org/10.1007/s12517-020-05715-x

Nur, N., Wajidi, F., Sulfayanti, S., & Wildayani, W. (2023). Implementasi Algoritma Random Forest Regression untuk Memprediksi Hasil Panen Padi di Desa Minanga. Jurnal Komputer Terapan, 9(1), 58–64. https://doi.org/10.35143/jkt.v9i1.5917

Orji, U., & Ukwandu, E. (2024). Machine learning for an explainable cost prediction of medical insurance. Machine Learning with Applications, 15(July 2023), 100516. https://doi.org/10.1016/j.mlwa.2023.100516

Pino-Vargas, E., Taya-Acosta, E., Ingol-Blanco, E., & Torres-Rúa, A. (2022). Deep Machine Learning for Forecasting Daily Potential Evapotranspiration in Arid Regions, Case: Atacama Desert Header. Agriculture (Switzerland), 12(12). https://doi.org/10.3390/agriculture12121971

Runtunuwu, E., H. Syahbuddin, & A. Pramudia. (2008). Validasi Model Pendugaan Evapotranspirasi: Upaya Melengkapi Sistem Database Iklim Nasional. Jurnal Tanah Dan Iklim, 27, 1–12.

Saidah, H., Sulistyono, H., & Budianto, M. B. (2020). Kalibrasi Persamaan Thornthwaite Dan Evaporasi Panci Untuk Memprediksi Evapotranspirasi Potensial Pada Daerah Dengan Data Cuaca Terbatas. Jurnal Sains Teknologi & Lingkungan, 6(1), 72–84. https://doi.org/10.29303/jstl.v6i1.155

Sofos, F., & Karakasidis, T. E. (2021). Nanoscale slip length prediction with machine learning tools. Scientific Reports, 11(1), 1–10. https://doi.org/10.1038/s41598-021-91885-x

Sua, L. S., Wang, H., Ortiz, J., Sua, L. S., Wang, H., Ortiz, J., Huang, J., & Alidaee, B. (2023). Predicting renewable energy production outputs from climate factors : A machine learning approach Predicting renewable energy production outputs from climate factors : A machine learning approach. 0–14.

Supangat, A. B. (2016). Analisis perubahan nilai pendugaan evapotranspirasi potensial akibat perubahan iklim di kawasan hutan tanaman eucalyptus pellita. Balai Penelitian Dan Pengembangan Teknologi Pengelolaan DAS,150 112–122.

Tanny, J. (2022). Evapotranspiration Measurements and Modeling. Water (Switzerland), 14(16), 16–18. https://doi.org/10.3390/w14162474

Wang, Y., Meili, N., & Fatichi, S. (2024). Ecohydrological responses to solar radiation changes. March, 1–24.

Yong, S. L. S., Ng, J. L., Huang, Y. F., & Ang, C. K. (2023). Estimation of Reference Crop Evapotranspiration with Three Different Machine Learning Models and Limited Meteorological Variables. Agronomy, 13(4). https://doi.org/10.3390/agronomy13041048

Downloads

Published

2025-04-30

How to Cite

Apriliani, L. M. P., Sulastri, N. N., Widia, I. W., & Budisanjaya, I. P. G. (2025). Estimasi Evapotranspirasi Potensial Menggunakan Algoritma Random Forest di Daerah Irigasi Tungkub, Bali. Jurnal BETA (Biosistem Dan Teknik Pertanian), 13(1), 142–150. https://doi.org/10.24843/j.beta.2025.v13.i01.p16

Issue

Vol. 13 No. 1 (2025): April

Section

Articles

License

Creative Commons License

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

License Term

All articles published in Jurnal Beta (Biosistem dan Teknik Pertanian)  are open access and licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This means that anyone is free to:

  • Share — copy and redistribute the material in any medium or format.

  • Adapt — remix, transform, and build upon the material for any purpose, even commercially.

However, this is granted under the following conditions:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

By submitting an article to Jurnal Beta (Biosistem dan Teknik Pertanian), authors agree to the publication of their work under this open access license. The authors retain the copyright of their work, but grant Jurnal Beta (Biosistem dan Teknik Pertanian)  the right of first publication.

For more information about the CC BY 4.0 license, please visit the official website: https://creativecommons.org/licenses/by/4.0/

Most read articles by the same author(s)

<< < 1 2 3 > >>