1,431
Views
17
CrossRef citations to date
0
Altmetric
Research Article

Prediction of reference evapotranspiration for irrigation scheduling using machine learning

, &
Pages 2669-2677 | Received 06 Feb 2020, Accepted 15 Jul 2020, Published online: 06 Nov 2020

References

  • Adamala, S., et al., 2019. Generalized wavelet neural networks for evapotranspiration modeling in India. ISH Journal of Hydraulic Engineering, 25 (2), 119–131. doi:10.1080/09715010.2017.1327825
  • Allen, R., et al., 1998. Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. Rome, Italy: Food and Agriculture Organization of the United Nations.
  • Bakhtiari, B., et al., 2011. Evaluation of reference evapotranspiration models for a semiarid environment using lysimeter measurements. Journal of Agricultural Science and Technology, 13, 223–237.
  • Caminha, H.D., et al., 2017. Estimating reference evapotranspiration using data mining prediction models and feature selection. International Conference on Enterprise Information Systems in ICEIS. 1, 272–279. doi:10.5220/0006327202720279
  • Cobaner, M., 2013. Reference evapotranspiration based on Class A pan evaporation via wavelet regression technique. Irrigation Science, 31 (2), 119–134. doi:10.1007/s00271-011-0297-x
  • Droogers, P. and Allen, R.G., 2002. Estimating reference evapotranspiration under inaccurate data conditions. Irrigation and Drainage Systems, 16 (1), 33–45. doi:10.1023/A:1015508322413
  • Falamarzi, Y., et al., 2014. Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks. Agricultural Water Management, 140, 26–36. doi:10.1016/j.agwat.2014.03.014
  • Feng, Y., et al., 2017. Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agricultural Water Management, 193, 163–173. doi:10.1016/j.agwat.2017.08.003
  • Ferreira, L.B. and da Cunha, F.F., 2020. New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning. Agricultural Water Management, 234, 106113. doi:10.1016/j.agwat.2020.106113
  • Hashemi, M. and Sepaskhah, A.R., 2020. Evaluation of artificial neural network and Penman–Monteith equation for the prediction of barley standard evapotranspiration in a semi-arid region. Theoretical and Applied Climatology, 139 (1), 275–285. doi:10.1007/s00704-019-02966-x
  • Huntington, T.G. and Billmire, M., 2014. Trends in precipitation, runoff, and evapotranspiration for rivers draining to the Gulf of Maine in the United States. Journal of Hydrometeorology, 15 (2), 726–743. doi:10.1175/JHM-D-13-018.1
  • Ji, S., et al., 2018. 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sensing. (Basel), 10 (2), 75. doi:10.3390/rs10010075
  • Jia, Y., et al. 2014. Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on Multimedia, New York, USA. 675–678. November.
  • Keshtegar, B., Kisi, O., and Zounemat-Kermani, M., 2019. Polynomial chaos expansion and response surface method for nonlinear modelling of reference evapotranspiration. Hydrological Sciences Journal, 64 (6), 720–730. doi:10.1080/02626667.2019.1601727
  • Khoob, A.R., 2008. Artificial neural network estimation of reference evapotranspiration from pan evaporation in a semi-arid environment. Irrigation Sciences, 27 (1), 35–39. doi:10.1007/s00271-008-0119-y
  • Kim, S. and Kim, H.S., 2008. Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modelling. Journal of Hydrology, 351, 299–317. doi:10.1016/j.jhydrol.2007.12.014
  • Kisi, Ö. and Yildirim, G., 2005. Forecasting of reference evapotranspiration by artificial neural networks. Journal of Irrigation and Drainage Engineering, 131 (4), 390–391. doi:10.1061/(ASCE)0733-9437
  • Kisi, O., 2011. Modelling reference evapotranspiration using evolutionary neural networks. Journal of Irrigation and Drainage Engineering, 137 (10), 636–643. doi:10.1061/(ASCE)IR.1943-4774.0000333
  • Kisi, O., 2013. Least squares support vector machine for modelling daily reference evapotranspiration. Irrigation Sciences, 31 (4), 611–619. doi:10.1061/(ASCE)0733-9437
  • Kovoor, G.M. and Nandagiri, L., 2018. Sensitivity analysis of FAO-56 Penman–Monteith reference evapotranspiration estimates using Monte Carlo simulations. In: Dr. V.P. Singh, Prof. Dr. S. Yadav, Prof. Dr. R.N. Yadava, eds. Hydrological modelling. Singapore: Springer, 73–84.
  • Kramer, R., et al., 2015. Evapotranspiration trends over the eastern United States during the 20th century. Hydrology, 2 (2), 93–111. doi:10.3390/hydrology2020093
  • Krizhevsky, A., 2012. H., Hinton, GE: imagenet classification with deep CNN. Advances in Neural Information Processing Systems, 60 (6), 1097–1105.
  • Kumar, M., Raghuwanshi, N.S., and Singh, R., 2011. Artificial neural networks approach in evapotranspiration modeling: a review. Irrigation Sciences, 29, 11–25. doi:10.1007/s00271-010-0230-8
  • Ladlani, I., et al., 2012. Modeling daily reference evapotranspiration (ET 0) in the north of Algeria using generalized regression neural networks (GRNN) and radial basis function neural networks (RBFNN): a comparative study. Meteorology and Atmospheric Physics, 118 (3–4), 163–178. doi:10.1007/s00703-012-0205-9
  • Manikumari, N., Murugappan, A., and Vinodhini, G., 2017. Time series forecasting of daily reference evapotranspiration by neural network ensemble learning for irrigation system. IOP Conference Series: Earth and Environmental Science, 80 (1), 012069. July. doi:10.1088/1755-1315/80/1/012069
  • Naidu, D. and Majhi, B., 2019. Reference evapotranspiration modeling using radial basis function neural network in different agro-climatic zones of Chhattisgarh. Journal of Agrometeorology, 21 (3), 316–326.
  • Partal, T., 2009. Modeling evapotranspiration using discrete wavelet transform and neural networks. Hydrological Processes: An International Journal, 23 (25), 3545–3555. doi:10.1002/hyp.7448
  • Pereira, L.S., et al., 2015. Crop evapotranspiration estimation with FAO56: past and future. Agricultural Water Management, 147, 4–20. doi:10.1016/j.agwat.2014.07.031
  • Petković, D., et al., 2016. Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration. Theoretical and Applied Climatology, 125 (3–4), 555–563. doi:10.1007/s00704-015-1522-y
  • Ringnér, M., 2008. What is principal component analysis? Nature Biotechnology, 26 (3), 303–304. doi:10.1038/nbt0308-303
  • Saggi, M.K. and Jain, S., 2019. Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning. Computers and Electronics in Agriculture, 156, 387–398. doi:10.1016/j.compag.2018.11.031
  • Sanikhani, H., et al., 2019. Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios. Theoretical and Applied Climatology, 135 (1–2), 449–462. doi:10.1007/s00704-018-2390-z
  • Shamshirband, S., et al., 2016. Estimation of reference evapotranspiration using neural networks and cuckoo search algorithm. Journal of Irrigation and Drainage Engineering, 142 (2), 04015044. doi:10.1061/(ASCE)IR.1943-4774.0000949
  • Sharma, A.N. and Walter, M.T., 2014. Estimating long-term changes in actual evapotranspiration and water storage using a one-parameter model. Journal of Hydrology, 519 (B), 2312–2317. doi:10.1016/j.jhydrol.2014.10.014
  • Shiri, J., et al., 2014. Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. Computers and Electronics in Agriculture, 108, 230–241. doi:10.1016/j.compag.2014.08.007
  • Subedi, A., Chávez, J.L., and Andales, A.A., 2013. Preliminary performance evaluation of the Penman–Monteith evapotranspiration equation in southeastern Colorado. Hydrology Days-Department of Civil and Environmental Engineering, 970, 84–90.
  • Tabari, H. and HosseinzadehTalaee, P., 2013. Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Computing and Applications, 23 (2), 341–348. doi:10.1007/s00521-012-0904-7
  • Tikhamarine, Y., et al., 2019. Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches. Hydrological Sciences Journal, 64 (15), 15,1824–1842. doi:10.1080/02626667.2019.1678750
  • Trajkovic, S., 2009. Comparison of radial basis function networks and empirical equations for converting from pan evaporation to reference evapotranspiration. Hydrological Processes: An International Journal, 23 (6), 874–880. doi:10.1002/hyp.7221
  • Trigo, I.F., et al., 2018. Validation of reference evapotranspiration from Meteosat Second Generation (MSG) observations. Agricultural and Forest Meteorology, 259, 271–285. doi:10.1016/j.agrformet.2018.05.008
  • Valipour, M. and Sefidkouhi, M.A.G., 2018. Temporal analysis of reference evapotranspiration to detect variation factors. International Journal of Global Warming, 14 (3), 385–401. doi:10.1504/IJGW.2018.090403
  • Ventura, F., et al., 1999. An evaluation of common evapotranspiration equations. Irrigation Sciences, 18 (4), 163–170. doi:10.1007/s002710050058
  • Zotarelli, L., et al., 2010. Step by step calculation of the Penman-Monteith evapotranspiration (FAO-56 method). Institute of Food and Agricultural Sciences. University of Florida.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.