Abstract
Adopting methodologies utilizing exogenous data from ancillary stations for determining crop water requirement is a suitable approach to exempt local shortcomings due to the lack of meteorological data/stations. Meanwhile, soft computing techniques might be suitable tools to be used with such data management scenarios. The present paper aimed at evaluating the generalizability of the gene expression programming (GEP) technique for estimating reference evapotranspiration (ET0) through cross-station assessment and exogenous data supply, using data from Turkey and Iran. The GEP-based models were established and learnt using data from 10 stations in Turkey, and then the developed models were tested (validated) in 18 stations of Iran with considerable latitude differences. Different time periods (beginning and the end of time series) were selected for the training and testing stations so that there was no overlap among the dates of the events in both the groups. A comparison was also performed between the GEP models and the corresponding commonly used empirical equations. The obtained results revealed that the generalized GEP models presented promising outcomes in simulating daily ET0 values when they were trained and tested in quite distant stations with different chronological periods of the applied parameters. The performance accuracy of the empirical equations calibrated using exogenous data was reduced in comparison with their original (non-calibrated) versions. Further, although the generalization ability of the GEP models was reduced when the climatic context of the training-testing stations was different, the overall performance accuracy of those models was higher than those of the commonly used classic empirical equations.
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References
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration guideline for computing crop water requirements. FAO Irrigation and Drainage, Paper No. 56, Food and Agriculture Organization of the United Nations, Rome
Allen RG, Pereira LS, Howell TA, Jensen ME (2011) Evapotranspiration information reporting: I. Factors governing measurement accuracy. Agric. Water Manage. 98:899–920. https://doi.org/10.1016/j.agwat.2010.12.015
Citakoglu H, Cobaner M, Haktanir T, Kisi O (2014) Estimation of monthly mean reference evapotranspiration in Turkey. Water Resources Management 28:99–113. https://doi.org/10.1007/s11269-013-0474-1
Droogers P, Allen RG (2002) Estimating reference evapotranspiration under inaccurate data conditions. Irrig. Drain. Syst. 16(1):33–45
Duffie JA, Beckman WA (1991) Solar engineering of thermal processes, 2nd edn. John Wiley and Sons, NY
Feng Y, Cui N, Gong D, Zhang Q, Zhao L (2017) Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling. Agricultural Water Management 193:163–173. https://doi.org/10.1016/j.agwat.2017.08.003
Ferreira LB, da Cunha FF, de Oliveira RA, Fernandes Filho EI (2019) Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – a new approach. Journal of Hydrology 572:556–570. https://doi.org/10.1016/j.jhydrol.2019.03.028
Gunston H, Batchelor CH (1983) A comparison of the Priestley-Taylor and Penman methods for estimating reference crop evapotranspiration in tropical countries. Agric. Water Manage. 6(1):65–77
Hargreaves GL, Samani ZA (1982) Estimating potential evapotranspiration. J. Irrig. Drain. Eng. ASCE 108(3):225–230
Jing W, Yaseen ZM, Shahid S, Saggi MK, Tao H, Kisi O, Salih SQ, al-Ansari N, Chau KW (2019) Implementation of evolutionary computing models for reference evapotranspiration modeling: short review, assessment and possible future research directions. Engineering Applications of Computational Fluid Mechanics 13(1):811–823. https://doi.org/10.1080/19942060.2019.1645045
Keshtegar B, Heddam S, Sebbar A, Zhu SP, Trung NT (2019) SVR-RSM: a hybrid heuristic method for modeling monthly pan evaporation. Environ Sci Pollut Res 26:35807–35826
Kiafar H, Babazadeh H, Marti P, Kisi O, Landeras G, Karimi S, Shiri J (2017) Evaluating the generalizability of GEP models for estimating reference evapotranspiration in distant humid and arid locations. Theoretical and Applied Climatology 130(1-2):377–389. https://doi.org/10.1007/s00704-016-1888-5
Kisi O, Kilic Y (2016) An investigation on generalization ability of artificial neural networks and M5 model tree in modeling reference evapotranspiration. Theoretical and Applied Climatology 126(3-4):413–425. https://doi.org/10.1007/s00704-015-1582-z
Kisi O, Sanikhani H, Zounemat-Kermani M, Niazi F (2015) Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Computers and Electronics in Agriculture 115:66–77. https://doi.org/10.1016/j.compag.2015.04.015
Kumar M, Raghuwanshi NS, Singh R (2009) Development and validation of GANN model for evapotranspiration estimation. Journal of Hydrologic Engineering 14(2):131–140. https://doi.org/10.1061/(ASCE)1084-0699(2009)14:2(131)
Kumar M, Raghuwanshi NS, Singh R (2011) Artificial neural networks approach in evapotranspiration modeling: a review. Irrigation Sciences 29:11–25
Landeras G, Ortiz-Barredo A, Lopez JJ (2008) Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agirc. Water Manag. 95:553–565
Martínez-Cob A, Tejero-Juste M (2004) A wind-based qualitative calibration of the Hargreaves ET0 estimation equation in semiarid regions. Agric. Water Manage. 64(3):251–264
Mattar MA, Alazba AA (2018) GEP and MLR approaches for the prediction of reference evapotranspiration. Neural Computing and Applications. 31(10):5843–5855. https://doi.org/10.1007/s00521-018-3410-8
Mehdizadeh S, Behmanesh J, Khalili K (2017) Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Computers and Electronics in Agriculture 139:103–114. https://doi.org/10.1016/j.compag.2017.05.002
Mohammadrezapour O, Piri J, Kisi O (2019) Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran). Water Science and Technology: Water Supply 19(2):392–403. https://doi.org/10.2166/ws.2018.084
Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev. 100(2):81–92
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 Manag. 188:101–114
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
Shiri J (2019) Modeling reference evapotranspiration in island environments: assessing the practical implications. Journal of Hydrology 570:265–280. https://doi.org/10.1016/j.jhydrol.2018.12.068
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. Computers and Electronics in Agriculture. 108:230–241. https://doi.org/10.1016/j.compag.2014.08.007
Shiri J, Nazemi AH, Sadraddini AA, Marti P, Fakheri Fard A, Kisi O, Landeras G (2019) Alternative heuristics equations to the Priestley–Taylor approach: assessing reference evapotranspiration estimation. Theoretical and Applied Climatology 138(1-2):831–848. https://doi.org/10.1007/s00704-019-02852-6
Traore S, Guven A (2012) Regional-specific numerical models of evapotranspiration using gene-expression programming interface in Sahel. Water Resources Management 26(15):4367–4380. https://doi.org/10.1007/s11269-012-0149-3
Turc L (1961) Evaluation des besoinsen eau d'irrigation evapotranspiration potentielle. Ann. Agron. 12(1):13–49
UNEP (United Nations Environmental Programme) (1997) World atlas of desertification. Editorial commentary by N. Middleton and D.S.G. Thomas. London: Edward Arnold
Wang Z, Wu P, Zhao X, Cao X, Gao Y (2011) GANN models for reference evapotranspiration estimation developed with weather data from different climatic regions. Theoretical and applied climatology 116:481–489
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M.H.K. made the investigation, simulation, and validation phases. A.M. prepared the necessary resources and software and had contribution in modeling phase. O.K. helped with preparing some necessary data and had contribution in conceptualization and writing the paper. J.S. made the fundamental concept of the study and defined the methodology. He had contribution in writing the paper (both original and revised versions). All authors read and approved the final manuscript.
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Kazemi, M.H., Majnooni-Heris, A., Kisi, O. et al. Generalized gene expression programming models for estimating reference evapotranspiration through cross-station assessment and exogenous data supply. Environ Sci Pollut Res 28, 6520–6532 (2021). https://doi.org/10.1007/s11356-020-10916-8
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DOI: https://doi.org/10.1007/s11356-020-10916-8