Machine learning modeling of reference evapotranspiration in Central Luzon, Philippines
DOI:
https://doi.org/10.54386/jam.v27i4.2909Keywords:
Reference evapotranspiration, FAO Penman-Monteith, Machine learning, Central Luzon, Limited weather dataAbstract
Reference evapotranspiration (ETo) is crucial for calculating irrigation requirements. Instruments that directly measure ETo are still costly and limited while the empirical models are data intensive. Meteorological data of Central Luzon, Philippines (1985-2019) were used to estimate ETo using the FAO Penman-Monteith method. The performances of machine learning algorithms in estimating ETo were analyzed using ground-based weather data. Optimal models were determined using decision thresholds (RMSE<0.39 mm day-1, R2>0.75, MSE<0.15 mm day-1, MAE<0.30 mm day-1). The models were further assessed using principal component analysis for finding relevant variables (σ2=0.95) and the Wilcoxon test for comparing two samples (α=0.05). Results show that optimal model required only two or three weather variables depending on the station. In general, the algorithms can be ranked as follows: Gaussian progress regression, Neural network, Support vector machines, Ensemble of trees, Regression trees, and Linear regression. The study reveals that machine learning can accurately predict ETo using ground-based weather data, and it can be a good alternative to data-intensive empirical models.
References
Addinsoft. (2020). XLSTAT support center: Wilcoxon signed rank test on two paired samples in excel tutorial. https://help.xlstat.com/s/article/wilcoxon-signed-rank-test-on-two-paired-samples-in-excel-tutorial?language=en_US
Allen, R., Pereira, L., Raes, D., and Smith, M. (1998). Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56. Food and Agriculture Organization of the United Nations. https://www.fao.org/4/X0490E/x0490e00.htm
Bijlwan, Amit, Shweta Pokhriyal, Ranjan, R., R.K Singh, and Ankita Jha. (2024). Machine learning methods for estimating reference evapotranspiration. J. Agrometeorol., 26(1): 63–68. https://doi.org/10.54386/jam.v26i1.2462
Caguiat, L. S., Saludes, R. B., Castro, M. L. Y., and Lampayan, R. M. (2022). Spatio-Temporal Trend Analysis of Reference Evapotranspiration in Central Luzon, Philippines. Philippine J. Sci., 152 (S1): 33–46. https://philjournalsci.dost.gov.ph/images/pdf/pjs_pdf/vol152_S1_Meteorology/spatio-temporal_trend_analysis_of_reference_evapotranspiration_.pdf
Darshana, Pandey, A., and Pandey, R. P. (2013). Analysing trends in reference evapotranspiration and weather variables in the Tons River Basin in Central India. Stoch. Environ. Res. Risk Assess., 27(6):1407–1421. https://doi.org/10.1007/s00477-012-0677-7
Dou, X., and Yang, Y. (2018). Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems. Advan. Meteorol., 1–18. https://doi.org/10.1155/2018/1824317
El-Shirbeny, M. A. (2016). Evaluation Of Hargreaves Based on Remote Sensing Method To Estimate Potential Crop Evapotranspiration. Intern. J. Geom., 11(23): 2143-2149. https://doi.org/10.21660/2016.23.1122
Feng, K., and Tian, J. (2021). RETRACTED ARTICLE: Forecasting reference evapotranspiration using data mining and limited climatic data. Europ. J. Remote Sens., 54(sup2):363–371. https://doi.org/10.1080/22797254.2020.1801355
Granata, F. (2019). Evapotranspiration evaluation models based on machine learning algorithms—A comparative study. Agric. Water Manag., 217: 303–315. https://doi.org/10.1016/j.agwat.2019.03.015
Hargreaves, G. H., and Allen, R. G. (2003). History and Evaluation of Hargreaves Evapotranspiration Equation. J. Irrig. Drain. Engg., 129(1):53–63. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:1(53)
Karimi, S., Kisi, O., Kim, S., Nazemi, A. H., and Shiri, J. (2017). Modelling daily reference evapotranspiration in humid locations of South Korea using local and cross‐station data management scenarios. Intern. J. Climatol., 37(7): 3238–3246. https://doi.org/10.1002/joc.4911
Khan, Y., Kumar, V., Setiya, P., and Satpathi, A. (2023). Comparison of phenological weather indices based statistical, machine learning and hybrid models for soybean yield forecasting in Uttarakhand. J. Agrometeorol., 25(3): 425–431. https://doi.org/10.54386/jam.v25i3.2232
MathWorks. (2021). Principal component analysis of raw data (Matlab R2021a documentation). https://www.mathworks.com/help/stats/pca.html;jsessionid=54b1f99cfeb0c309c7272a02abf8
Mooi, E., and Sarstedt, M. (2011). A Concise Guide to Market Research. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-12541-6
PAGASA. (2020). Philippine Atmospheric, Geophysical and
Astronomical Services Administration. Climatological data from DOST-PAGASA. http://bagong.pagasa.dost.gov.ph/climate/climate-data
Shah, T. (2017). Train, validation, and test sets. https://tarangshah.com/blog/2017-12-03/train-validation-and-test-sets/
Valipour, M., Gholami Sefidkouhi, M. A., Raeini-Sarjaz, M., and Guzman, S. M. (2019). A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates. Atmos., 10(6): 311. https://doi.org/10.3390/atmos10060311
Wu, L., Peng, Y., Fan, J., and Wang, Y. (2019). Machine learning models for the estimation of monthly mean daily reference evapotranspiration based on cross-station and synthetic data. Hydrol. Res., 50(6): 1730–1750. https://doi.org/10.2166/nh.2019.060
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2025 LEA S. CAGUIAT, RONALDO B. SALUDES, MARION LUX Y. CASTRO , RUBENITO M. LAMPAYAN LAMPAYAN
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is a human-readable summary of (and not a substitute for) the license. Disclaimer.
You are free to:
Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material
The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
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.
NonCommercial — You may not use the material for commercial purposes.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
