Machine-Learning Models to Improve Accuracy of Real-Time Reference Evapotranspiration Estimates in an Arid Environment
Publication: Journal of Irrigation and Drainage Engineering
Volume 148, Issue 11
Abstract
Studies of the estimation of reference evapotranspiration () in Iran are mostly related to areas with humid and semiarid climates and less related to arid areas. On the other hand, few studies in arid regions have reported high root-mean square error (RMSE) values. However, these regions make an important contribution to agricultural production, and thus, water management of these regions is crucial. It motivated the implementation of such a study in an arid environment of Karaj, Iran, as a case study, in order to estimate daily with as much accuracy as possible. To achieve this purpose, the performance of 21 known models estimating , including 9 empirical models and 12 machine learning (ML) models, were evaluated. The method provided by the food and agriculture organization (FAO) known as FAO-56 Penman-Monteith (FPM) was regarded as the main reference method for measuring . A new climate data set related to the period of 2005–2020 (April–September) was used to calibrate and cross-validate models. In fact, this study intended to develop an approach for simulating day-to-day variations in in arid environments by benefitting minimal weather data (i.e., temperature, humidity, and wind speed) for practical purposes because most regions suffer from a lack of weather data, especially radiation data. Therefore, the performance of the best models calibrated in Karaj station was also validated based on data recorded in a research field with a similar climate during 2020–2021. The cross-validated results showed that the deep-learning (DL) model had the lowest RMSE as well as the highest compared with other models. As averaged over all months in Karaj, the DL model exhibited a RMSE 64% less than the best-calibrated empirical model (i.e., Valiantzas-VTS), which is a solar radiation–based model. Furthermore, the improvements arising from using the DL model were more considerable in the extremes than in the middle values. The findings of the field research also demonstrated that the approach developed in the current study would be beneficial at locations other than the calibration station. This approach requires few inputs, is independent of solar radiation data, and also has the lowest RMSE compared with that of other studies. For future studies, the combination of the daily estimator developed in this study with a recently developed empirical approach to estimate crop coefficients is suggested to manage irrigation on croplands if arid regions.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The data set used in the current study for daily time period of 2005–2020 at Karaj station that supports the findings of this study is available from the corresponding author upon reasonable request.
Acknowledgments
The authors greatly appreciate the respectful editor and reviewers for their useful comments, which we believe have noticeably improved the paper’s presentation.
References
Abdullah, S. S. A., M. A. Malek, N. S. Abdullah, O. Kisi, and K. S. Yap. 2015. “Extreme learning machines: A new approach for prediction of reference evapotranspiration.” J. Hydol. 527 (Apr): 184–195. https://doi.org/10.1016/j.jhydrol.2015.04.073.
Aghajanloo, M. B., A. A. Sabziparvar, and P. HosseinzadehTalaee. 2013. “Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semiarid region of Iran.” Neural Comput. Appl. 23 (5): 1387–1393. https://doi.org/10.1007/s00521-012-1087-y.
Akhavan, S., F. Mousabeygi, and M. C. Peel. 2018. “Assessment of eight reference evapotranspiration () methods considering Köppen climate class in Iran.” Hydrol. Sci. J. 63 (10): 1468–1481. https://doi.org/10.1080/02626667.2018.1513654.
Allen, R. G., L. S. Pereira, D. Raes, and M. Smith. 1998. “Crop evapotranspiration.” In Guide lines for computing crop evapotranspiration. Food and Agriculture Organization (FAO) irrigation and drainage paper no. 56. Rome: FAO.
Allen, R. G., M. Smith, and L. S. Pereira. 1994. “An update for the definition of reference evapotranspiration.” ICID Bull. 43 (2): 1–34.
Angstrom, A. K. 1924. “Solar and terrestrial radiation.” Q. J. R. Meteorol. Soc. 50 (210): 121–126. https://doi.org/10.1002/qj.49705021008.
Asong, Z. E., M. N. Khaliq, and H. S. Wheater. 2016. “Multisite multivariate modeling of daily precipitation and temperature in the Canadian Prairie Provinces using generalized linear models.” Clim. Dyn. 47 (9): 2901–2921. https://doi.org/10.1007/s00382-016-3004-z.
Bai, S., J. Z. Kolter, and V. Koltun. 2018. “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling.” Preprint, submitted March 4, 2018. https://arxiv.org/abs/1803.01271.
Bui, Q. T., Q. H. Nguyen, X. L. Nguyen, V. D. Pham, H. D. Nguyen, and V. M. Pham. 2020. “Verification of novel integrations of swarm intelligence algorithms into deep learning.” J. Hydrol. 581 (Feb): 124379. https://doi.org/10.1016/j.jhydrol.2019.124379.
Chen, Z., Z. Zhu, H. Jiang, and S. Sun. 2020. “Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods.” J. Hydrol. 591 (Dec): 125286. https://doi.org/10.1016/j.jhydrol.2020.125286.
Citakoglu, H., M. Cobaner, T. Haktanir, and O. Kisi. 2014. “Estimation of monthly mean reference evapotranspiration in Turkey.” Water Resour. Manage. 28 (1): 99–113. https://doi.org/10.1007/s11269-013-0474-1.
De Bruin, H. A. R., and W. N. Lablans. 1998. “Reference crop evapotranspiration determined with a modified Makkink equation.” Hydrol. Processes 12 (7): 1053–1062. https://doi.org/10.1002/(SICI)1099-1085(19980615)12:7%3C1053::AID-HYP639%3E3.0.CO;2-E.
Djaman, K., A. B. Balde, A. Sow, B. Muller, S. Irmak, M. K. N’Diaye, and K. Saito. 2015. “Evaluation of sixteen reference evapotranspiration methods under Sahelian conditions in the Senegal River Valley.” J. Hydol.: Reg. Stud. 3 (Mar): 139–159.
Doorenbos, J., and W. O. Pruitt. 1977. Crop water requirements. Food and Agriculture Organization (FAO) irrigation and drainage paper no. 24. Rome: FAO.
dos Santos Farias, D. B., D. Althoff, L. N. Rodrigues, and R. Filgueiras. 2020. “Performance evaluation of numerical and machine learning methods in estimating reference evapotranspiration in a Brazilian agricultural frontier.” Theor. Appl. Climatol. 142 (3): 1481–1492. https://doi.org/10.1007/s00704-020-03380-4.
Dou, X., and Y. Yang. 2018. “Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems.” Comput. Electron. Agric. 148 (May): 95–106. https://doi.org/10.1016/j.compag.2018.03.010.
Draper, N., and H. Smith. 1981. Applied regression analysis. 2nd ed. New York: Wiley.
Fan, J., W. Yue, L. Wu, F. Zhang, H. Cai, X. Wang, X. Lu, and Y. Xiang. 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 (Dec): 225–241. https://doi.org/10.1016/j.agrformet.2018.08.019.
Farooque, A. A., H. Afzaal, F. Abbas, M. Bos, J. Maqsood, X. Wang, and N. Hussain. 2022. “Forecasting daily evapotranspiration using artificial neural networks for sustainable irrigation scheduling.” Irrig. Sci. 40 (1): 55–69. https://doi.org/10.1007/s00271-021-00751-1.
Feng, Y., N. Cui, D. Gong, Q. Zhang, and L. Zhao. 2017a. “Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modeling.” Agric. Water Manage. 193 (Nov): 163–173. https://doi.org/10.1016/j.agwat.2017.08.003.
Feng, Y., N. Cui, L. Zhao, X. Hu, and D. Gong. 2016. “Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China.” J. Hydol. 536 (May): 376–383. https://doi.org/10.1016/j.jhydrol.2016.02.053.
Feng, Y., Y. Peng, N. Cui, D. Gong, and K. Zhang. 2017b. “Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data.” Comput. Electron. Agric. 136 (Apr): 71–78. https://doi.org/10.1016/j.compag.2017.01.027.
Ferreira, L. B., and F. F. da Cunha. 2020. “New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning.” Agric Water Manage. 234 (May): 106113. https://doi.org/10.1016/j.agwat.2020.106113.
Ferreira, L. B., F. F. da Cunha, R. A. de Oliveira, and E. I. Fernandes Filho. 2019. “Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM—A new approach.” J. Hydol. 572 (May): 556–570. https://doi.org/10.1016/j.jhydrol.2019.03.028.
Fujun, W., Q. Mengwen, W. Huaguo, and Z. Changjin. 1996. “The response of winter wheat to water stress and nitrogen fertilizer use efficiency.” In Nuclear techniques to assess irrigation schedules for field crops, 51–61. Vienna, Austria: International Atomic Energy Agency.
George, B. A., B. R. S. Reddy, N. S. Raghuwanshi, and W. W. Wallender. 2002. “Decision support system for estimating reference evapotranspiration.” J. Irrig. Drain. Eng. 128 (1): 1–10. https://doi.org/10.1061/(ASCE)0733-9437(2002)128:1(1).
Ghaderi, A., M. Dasineh, M. Shokri, and J. Abraham. 2020. “Estimation of actual evapotranspiration using the remote sensing method and SEBAL algorithm: A case study in Ein Khosh Plain, Iran.” Hydrology 7 (2): 36. https://doi.org/10.3390/hydrology7020036.
Ghamghami, M., and J. P. Beiranvand. 2022. “Rainfed crop yield response to climate change in Iran.” Reg. Environ. Change 22 (1): 3. https://doi.org/10.1007/s10113-021-01856-1.
Ghamghami, M., N. Ghahreman, P. Irannejad, and K. Ghorbani. 2018. “Comparison of datamining and GDD-based models in discrimination of maize phenology.” Int. J. Plant Prod. 13 (1): 11–22. https://doi.org/10.1007/s42106-018-0030-2.
Ghamghami, M., and P. Irannejad. 2019. “An analysis of droughts in Iran during 1988–2017.” SN Appl. Sci. 1 (10): 1217. https://doi.org/10.1007/s42452-019-1258-x.
Gocic, M., and S. Trajkovic. 2010. “Software for estimating reference evapotranspiration using limited weather data.” Comput. Electron. Agric. 71 (2): 158–162. https://doi.org/10.1016/j.compag.2010.01.003.
Hu, Q. F., D. W. Yang, Y. T. Wang, and H. B. Yang. 2011. “Global calibration of Hargreaves equation and its applicability in China.” [In Chinese.] Adv. Water Sci. 22 (2): 160–167.
Kalteh, A. M. 2016. “Improving forecasting accuracy of streamflow time series using least squares support vector machine coupled with data-preprocessing techniques.” Water Resour. Manage. 30 (2): 747–766. https://doi.org/10.1007/s11269-015-1188-3.
Kazemi, A., N. Ghahreman, M. Ghamghami, and A. Ghameshloo. 2021. “Application of Bayesian model averaging (BMA) approach for estimating evapotranspiration in Gorganrood-Gharesoo Basin, Iran.” J. Agric. Sci. Technol. 23 (6): 1395–1409.
Khoshravesh, M., M. A. G. Sefidkouhi, and M. Valipour. 2015. “Estimation of reference evapotranspiration using multivariate fractional polynomial, Bayesian regression, and robust regression models in three arid environments.” Appl. Water Sci. 7 (4): 1911–1922. https://doi.org/10.1007/s13201-015-0368-x.
Kim, S., J. Shiri, and O. Kisi. 2012. “Pan evaporation modeling using neural computing approach for different climatic zones.” Water Resour. Manage. 26 (11): 3231–3249. https://doi.org/10.1007/s11269-012-0069-2.
Kogan, F. 2020. Remote sensing for food security. New York: Springer. https://doi.org/10.1007/978-3-319-96256-6.
Landeras, G., A. Ortiz-Barredo, and J. J. Lopez. 2008. “Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (northern Spain).” Agric. Water Manage. 95 (5): 553–565. https://doi.org/10.1016/j.agwat.2007.12.011.
Landman, W. A., and S. J. Mason. 1999. “Operational long-lead prediction of South African rainfall using canonical correlation analysis.” Int. J. Climatol. 19 (10): 1073–1090. https://doi.org/10.1002/(SICI)1097-0088(199908)19:10%3C1073::AID-JOC415%3E3.0.CO;2-J.
Lee, T., J. Y. Shin, J. S. Kim, and V. P. Singh. 2020. “Stochastic simulation on reproducing longterm memory of hydroclimatological variables using deep learning model.” J. Hydrol. 582 (Mar): 124540. https://doi.org/10.1016/j.jhydrol.2019.124540.
Li, Z., T. Huffman, A. Zhang, F. Zhou, and B. McConkey. 2012. “Spatially locating soil classes within complex soil polygons—Mapping soil capability for agriculture in Saskatchewan Canada.” Agric. Ecosyst. Environ. 152 (5): 59–67. https://doi.org/10.1016/j.agee.2012.02.007.
Maselli, F., L. Angeli, M. Chiesi, L. Fibbi, B. Rapi, M. Romani, F. Sabatini, and P. Battista. 2020. “An improved NDVI-based method to predict actual evapotranspiration of irrigated grasses and crops.” Agric. Water Manage. 233 (Apr): 106077. https://doi.org/10.1016/j.agwat.2020.106077.
Mitchell, T. M. 1997. Machine learning. New York: McGraw-Hill International.
Niaghi, A. R., O. Hassanijalilian, and J. Shiri. 2021. “Estimation of reference evapotranspiration using spatial and temporal machine learning approaches.” Hydrology 8 (1): 25. https://doi.org/10.3390/hydrology8010025.
Pérez, J. Á. M., S. G. García-Galiano, B. Martin-Gorriz, and A. Baille. 2017. “Satellite-based method for estimating the spatial distribution of crop evapotranspiration: Sensitivity to the Priestley-Taylor coefficient.” Remote Sens. 9 (6): 611. https://doi.org/10.3390/rs9060611.
Perugu, M., A. J. Singam, and C. S. R. Kamasani. 2013. “Multiple linear correlation analysis of daily reference evapotranspiration.” Water Resour. Manage. 27 (5): 1489–1500. https://doi.org/10.1007/s11269-012-0250-7.
Pourmeidani, A., M. Ghamghami, H. Olya, and N. Ghahreman. 2020. “Determination of suitable regions for cultivation of three medicinal plants under a changing climate.” Environ. Processes 7 (1): 89–108. https://doi.org/10.1007/s40710-020-00423-w.
Priestley, C. H. B., and R. J. Taylor. 1972. “On the assessment of surface heat flux and evaporation using large-scale parameters.” Mon. Weather Rev. 100 (2): 81–92. https://doi.org/10.1175/1520-0493(1972)100%3C0081:OTAOSH%3E2.3.CO;2.
Raziei, T. 2018. “An analysis of daily and monthly precipitation seasonality and regimes in Iran and the associated changes in 1951–2014.” Theor. Appl. Climatol. 134 (3–4): 913–934. https://doi.org/10.1007/s00704-017-2317-0.
Reis, M. M., A. J. da Silva, J. Z. Junior, L. D. T. Santos, A. M. Azevedo, and É. M. G. Lopes. 2019. “Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data.” Comput. Electron. Agric. 165 (Oct): 104937. https://doi.org/10.1016/j.compag.2019.104937.
Sabziparvar, A. A., and H. Tabari. 2010. “Regional Estimation of reference evapotranspiration in arid and semiarid regions.” J. Irrig. Drain. Eng. 136 (10): 724–731. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000242.
Sanford, W. E., and D. L. Selnick. 2013. “Estimation of evapotranspiration across the conterminous United States using a regression with climate and land-cover data.” J. Am. Water Resour. Assoc. 49 (1): 217–230. https://doi.org/10.1111/jawr.12010.
Shapiro, S. S., and M. B. Wilk. 1965. “An analysis of variance test for normality (complete samples).” Biometrika 52 (3–4): 591–611. https://doi.org/10.1093/biomet/52.3-4.591.
Shiri, J. 2017. “Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran.” Agric. Water Manage. 188 (Jul): 101–114. https://doi.org/10.1016/j.agwat.2017.04.009.
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 (Jun): 737–750. https://doi.org/10.1016/j.jhydrol.2018.04.042.
Shiri, J., A. H. Nazemi, A. A. Sadraddini, G. Landeras, O. Kisi, A. Fakheri Fard, and P. Marti. 2014. “Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran.” Comput. Electron. Agric. 108 (Oct): 230–241. https://doi.org/10.1016/j.compag.2014.08.007.
Shiri, J., A. A. Sadraddini, A. H. Nazemi, O. Kisi, P. M. A. F. Fard, and G. Landeras. 2013. “Evaluation of different data management scenarios for estimating daily reference evapotranspiration.” Hydrol. Res. 44 (6): 1058–1070. https://doi.org/10.2166/nh.2013.154.
Szegedy, C., A. Toshev, and D. Erhan. 2013. “Deep neural networks for object detection.” Adv. Neural Inf. Process. Syst. 26: 2553–2561.
Tabari, H., M. A. Grismer, and S. Trajkovic. 2013. “Comparative analysis of 31 reference evapotranspiration methods under humid conditions.” Irrig. Sci. 31 (2): 107–117. https://doi.org/10.1007/s00271-011-0295-z.
Tabari, H., O. Kisi, A. Ezani, and P. Hosseinzadeh Talaee. 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 (Jun): 78–89. https://doi.org/10.1016/j.jhydrol.2012.04.007.
Valiantzas, J. D. 2012. “Simplified reference evapotranspiration formula using an empirical impact factor for Penman’s aerodynamic term.” J. Hydrol. Eng. 18 (1): 108–114. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000590.
Vapnik, V. 1995. The nature of statistical learning theory. New York: Springer.
Wang, N., D. Zhang, H. Chang, and H. Li. 2020. “Deep learning of subsurface flow via theory guided neural network.” J. Hydol. 584 (May): 124700. https://doi.org/10.1016/j.jhydrol.2020.124700.
Wang, Y. X. 2019. Comparison of machine learning and empirical models for estimating spring maize evapotranspiration in the case of Shenyang areas. [In Chinese.] Shenyang, China: Shenyang Agricultural Univ.
Ward, M. D., and J. S. Ahlquist. 2018. Maximum likelihood for social science: Strategies for analysis. New York: Cambridge University Press.
Wen, X., J. Si, Z. He, J. Wu, H. Shao, and H. Yu. 2015. “Support-vector-machine-based models for modeling daily reference evapotranspiration with limited climatic data in extreme arid regions.” Water Resour. Manage. 29 (9): 3195–3209. https://doi.org/10.1007/s11269-015-0990-2.
Wu, L., and J. Fan. 2019. “Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration.” PLoS One 14 (5): e0217520. https://doi.org/10.1371/journal.pone.0217520.
Yin, Z., Q. Feng, L. Yang, R. C. Deo, X. Wen, J. Si, and S. Xiao. 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 (11): 880. https://doi.org/10.3390/w9110880.
Zanetti, S. S., E. F. Sousa, V. P. S. Oliveira, F. T. Almeida, and S. Bernardo. 2007. “Estimating evapotranspiration using artificial neural network and minimum climatological data.” J. Irrig. Drain. Eng. 133 (2): 83–89. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:2(83).
Zhang, J., Y. Zhu, X. Zhang, M. Ye, and J. Yang. 2018. “Developing a Long short-term memory (LSTM) based model for predicting water table depth in agricultural areas.” J. Hydrol. 561 (Jun): 918–929. https://doi.org/10.1016/j.jhydrol.2018.04.065.
Zheng, H., and A. Kusiak. 2009. “Prediction of wind farm power ramp rates: A data-mining approach.” J. Sol. Energy Eng. 131 (3): 031011. https://doi.org/10.1115/1.3142727.
Zhou, Y. C., T. Y. Xu, W. Zhen, and H. B. Deng. 2017. “Classification and recognition approaches of tomato main organs based on DCNN.” [In Chinese.] Trans. CSAE 33 (15): 219–226.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Feb 10, 2022
Accepted: Jun 24, 2022
Published online: Sep 15, 2022
Published in print: Nov 1, 2022
Discussion open until: Feb 15, 2023
ASCE Technical Topics:
- Arid lands
- Artificial intelligence and machine learning
- Calibration
- Case studies
- Climates
- Computer programming
- Computing in civil engineering
- Engineering fundamentals
- Environmental engineering
- Field tests
- Infrastructure
- Irrigation engineering
- Measurement (by type)
- Meteorology
- Methodology (by type)
- Research methods (by type)
- Tests (by type)
- Urban and regional development
- Water and water resources
- Weather forecasting
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.