Abstract
Reference evapotranspiration (ET0) is a major factor for water resource management. Although the FAO Penman–Monteith model is the highly recommended for estimating ET0, its requirement of a complete climatic variables has made the application of this model complicated. The objective of this study was to investigate the potential of four machine learning (ML) models, namely extreme learning machine (ELM), genetic programming (GP), random forest (RF), and support vector regression (SVR), for estimating daily ET0 with limited climatic data using a tenfold cross-validation method across different climate zones in New Mexico. Four input scenarios, namely S1 (Tmax (maximum air temperature), Tmin (minimum air temperature), RHave (average relative humidity), U2 (wind speed at 2 m height), RS (total solar radiation)), S2 (Tmax, Tmin, U2, RS), S3 (Tmax, Tmin, RS), and S4 (Tave, RS), were considered using climatic data during the 2009–2019 period from six selected weather stations across different climate zones. The results showed that the estimated daily ET0 differed significantly following ML model types and input scenarios across different climate zones. The ML models under S1 scenario showed the best estimation accuracy during the testing stage in climate zones 1 and 5 (RMSE and MAE < 0.5 mm day−1). The ML models under S3 and S4 scenarios were found to be more preferred at climate zones 1, 5, and 8 (RMSE and MAE < 1 mm day−1). The estimation accuracy of ML models was decreased with lack of RHave and U2 data in input scenarios although the ML models based on S4 scenario (only Tave and RS) showed acceptable ET0 estimations particularly in the climate zone 5 (0.5 mm day−1 < RMSE < 0.6 mm day−1). The SVR and ELM were the best ML models for all input scenarios in the studied climate zones where these models showed the best stabilities in the testing stages.
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References
Abdullah SS, Malek MA, Abdullah NS, Kisi O, Yap KS (2015) Extreme learning machines: a new approach for prediction of reference evapotranspiration. J Hydrol 527:184–195. https://doi.org/10.1016/j.jhydrol.2015.04.073
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 Fao. Rome 300:D05109
Antonopoulos VZ, Antonopoulos AV (2017) Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables. Comput Electron Agric 132:86–96. https://doi.org/10.1016/j.compag.2016.11.011
Barzegar R, Asghari Moghaddam A, Adamowski J, Fijani E (2017) Comparison of machine learning models for predicting fluoride contamination in groundwater. Stoch Env Res Risk Assess 31:2705–2718. https://doi.org/10.1007/s00477-016-1338-z
Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Climate in New Mexico, NM climate center, NMSU. https://weather.nmsu.edu/climate/about/
Cortes C, Vapnik V (1995) Support-Vector Networks. Mach Learn 20:273–297. https://doi.org/10.1007/bf00994018
Despotovic M, Nedic V, Despotovic D, Cvetanovic S (2015) Review and statistical analysis of different global solar radiation sunshine models. Renew Sustain Energy Rev 52:1869–1880. https://doi.org/10.1016/j.rser.2015.08.035
Ding R, Kang S, Li F, Zhang Y, Tong L, Sun Q (2010) Evaluating eddy covariance method by large-scale weighing lysimeter in a maize field of northwest China. Agric Water Manag 98:87–95. https://doi.org/10.1016/j.agwat.2010.08.001
Fan J et al (2018) Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agric for Meteorol 263:225–241. https://doi.org/10.1016/j.agrformet.2018.08.019
Feng Y, Cui N, Gong D, Zhang Q, Zhao L (2017a) Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agric Water Manag 193:163–173. https://doi.org/10.1016/j.agwat.2017.08.003
Feng Y, Peng Y, Cui N, Gong D, Zhang K (2017b) Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Comput Electron Agric 136:71–78. https://doi.org/10.1016/j.compag.2017.01.027
Gocic M, Petković D, Shamshirband S, Kamsin A (2016) Comparative analysis of reference evapotranspiration equations modelling by extreme learning machine. Comput Electron Agric 127:56–63. https://doi.org/10.1016/j.compag.2016.05.017
Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96–99. https://doi.org/10.13031/2013.26773
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme Learning Machine: Theory and Applications. Neurocomputing 70:489–501. https://doi.org/10.1016/j.neucom.2005.12.126
Karl TR, WJ Koss (1984) U.S. climate divisions, NOAA. https://www.ncdc.noaa.gov/monitoring-references/maps/usclimate-divisions.php#history
Kisi O (2016) Modeling reference evapotranspiration using three different heuristic regression approaches. Agric Water Manag 169:162–172. https://doi.org/10.1016/j.agwat.2016.02.026
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection vol 1. MIT press,
Lin J-Y, Cheng C-T, Chau K-W (2006) Using support vector machines for long-term discharge prediction. Hydrol Sci J 51:599–612. https://doi.org/10.1623/hysj.51.4.599
Mokari E, Samani Z, Heerema R, Ward F (2021) Evaluation of long-term climate change impact on the growing season and water use of mature pecan in Lower Rio Grande Valley. Agric Water Manag 252:106893. https://doi.org/10.1016/j.agwat.2021.106893
Patil AP, Deka PC (2016) An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs. Comput Electron Agric 121:385–392. https://doi.org/10.1016/j.compag.2016.01.016
Romanenko VA (1961) Computation of the autumn soil moisture using a universal relationship for a large area Proc of Ukrainian Hydrometeorological Research Institute 3:12-25
Samani Z, Bawazir S, Skaggs R, Longworth J, Piñon A, Tran V (2011) A simple irrigation scheduling approach for pecans. Agric Water Manag 98:661–664. https://doi.org/10.1016/j.agwat.2010.11.002
Shiri J (2018) Improving the performance of the mass transfer-based reference evapotranspiration estimation approaches through a coupled wavelet-random forest methodology. J Hydrol 561:737–750. https://doi.org/10.1016/j.jhydrol.2018.04.042
Shiri J, Kişi Ö, Landeras G, López JJ, Nazemi AH, Stuyt LCPM (2012) Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain). J Hydrol 414–415:302–316. https://doi.org/10.1016/j.jhydrol.2011.11.004
Shiri J, Nazemi AH, Sadraddini AA, Landeras G, Kisi O, Fakheri Fard A, Marti P (2014) Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. Comput Electron Agric 108:230–241. https://doi.org/10.1016/j.compag.2014.08.007
Tabari H, Grismer ME, Trajkovic S (2013) Comparative analysis of 31 reference evapotranspiration methods under humid conditions. Irrig Sci 31:107–117. https://doi.org/10.1007/s00271-011-0295-z
Tabari H, Kisi O, Ezani A, HosseinzadehTalaee P (2012) SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. J Hydrol 444–445:78–89. https://doi.org/10.1016/j.jhydrol.2012.04.007
Torres AF, Walker WR, McKee M (2011) Forecasting daily potential evapotranspiration using machine learning and limited climatic data. Agric Water Manag 98:553–562. https://doi.org/10.1016/j.agwat.2010.10.012
Traore S, Luo Y, Fipps G (2016) Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages. Agric Water Manag 163:363–379. https://doi.org/10.1016/j.agwat.2015.10.009
Wang L, Kisi O, Zounemat-Kermani M, Li H (2017) Pan evaporation modeling using six different heuristic computing methods in different climates of China. J Hydrol 544:407–427. https://doi.org/10.1016/j.jhydrol.2016.11.059
Wen X, Si J, He Z, Wu J, Shao H, Yu H (2015) Support-vector-machine-based models for modeling daily reference evapotranspiration with limited climatic data in extreme arid regions. Water Resour Manag 29:3195–3209. https://doi.org/10.1007/s11269-015-0990-2
Yin Z, Feng Q, Yang L, Deo RC, Wen X, Si J, Xiao S (2017) Future projection with an extreme-learning machine and support vector regression of reference evapotranspiration in a mountainous inland watershed in north-west China Water 9 https://doi.org/10.3390/w9110880
Yin Z, Wen X, Feng Q, He Z, Zou S, Yang L (2016) Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area. Hydrol Res 48:1177–1191. https://doi.org/10.2166/nh.2016.205
Zhang D et al (2018) Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm. J Hydrol 565:720–736. https://doi.org/10.1016/j.jhydrol.2018.08.050
Zong Woo G, Joong Hoon K, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. SIMULATION 76:60–68. https://doi.org/10.1177/003754970107600201
Acknowledgements
The authors thank the National Institute of Food and Agriculture, US Department of Agriculture (award number: 2017-68007-26318) for supporting this work. The authors also thank New Mexico State University Climate Center for providing long-term climate data across New Mexico state.
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Conceptualization: E.M. and D.D.; Methodology: E.M.; H.M., and Z.S.; Technical investigation: D.D. and Z.S.; Writing (original draft preparation): E.M; Writing (review and editing): K.D.; Supervision: Z.S. All authors have read and agreed to the published version of the manuscript.
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Mokari, E., DuBois, D., Samani, Z. et al. Estimation of daily reference evapotranspiration with limited climatic data using machine learning approaches across different climate zones in New Mexico. Theor Appl Climatol 147, 575–587 (2022). https://doi.org/10.1007/s00704-021-03855-y
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DOI: https://doi.org/10.1007/s00704-021-03855-y