Abstract
Accurate estimation of evapotranspiration is one of the main aspects of water management. In this study, the capabilities of soft computing techniques for estimating daily evapotranspiration in Košice (Slovakia) were investigated. Daily solar radiation (SR), relative humidity (RH), air temperature (T), and wind speed (U) were the meteorological variables used for modeling. Based on the data, different combinations of multilayer perceptron (MLP), support vector regression (SVR), multilinear regression (MLR) models were generated. Model results are compared with each other and with the Hargreaves-Samani, Ritchie, and Turc empirical equations using three statistical criteria, namely mean square error (MSE), mean absolute relative error (MAE), and determination coefficient (R2). Of the empirical formulas applied, the Hargreaves-Samani equation gave the most compatible results with the Penman FAO 56 equation. Error percentage histograms were generated as a reference criterion. Model results show that the MLP model performs better than the other soft computing techniques used.
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References
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements. FAO - Food and Agriculture Organization of the United Nations, Rome
Bayazıt MOB (1998) Probability and statistics for engineers. Birsen Publishing House, Istanbul, Turkey
Bishop C (1995) Neural networks for pattern recognition. Oxford: University Press
Chen D (2012) Daily reference evapotranspiration estimation based on least squares support vector machines. In: IFIP Advances in Information and Communication Technology. https://doi.org/10.1007/978-3-642-27278-3_7
Cortes, C., Vapnik, V., 1995. Support-Vector Cortes, C., & Vapnik, V.. Support-vector networks. Machine Learning, 20(3), 273–297. doi:https://doi.org/10.1023/A:1022627411411
Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals, Syst 2. https://doi.org/10.1007/BF02551274
Demirci M, Üneş F, Aköz MS (2015) Prediction of cross-shore sandbar volumes using neural network approach. J. Mar. Sci. Technol. 20:171–179. https://doi.org/10.1007/s00773-014-0279-9
Fendeková M, Gauster T, Labudová L, Vrablíková D, Danáčová Z, Fendek M, Pekárová P (2018) Analysing 21st century meteorological and hydrological drought events in Slovakia. J. Hydrol. Hydromechanics 66:393–403. https://doi.org/10.2478/johh-2018-0026
Gavili S, Sanikhani H, Kisi O, Mahmoudi MH (2018) Evaluation of several soft computing methods in monthly evapotranspiration modelling. Meteorol. Appl. 25:128–138. https://doi.org/10.1002/met.1676
Gocić M, Motamedi S, Shamshirband S, Petković D, Ch S, Hashim R, Arif M (2015) Soft computing approaches for forecasting reference evapotranspiration. Comput. Electron. Agric. 113:164–173. https://doi.org/10.1016/j.compag.2015.02.010
Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl. Eng. Agric. 1(2):96–99
Haykin S (1999) Neural networks: a comprehensive. Pearson Education. 13:409–412. https://doi.org/10.1017/S0269888998214044
Hlaváčiková H, Novák V (2013) Comparison of daily potential evapotranspiration calculated by two procedures based on Penman-Monteith type equation. J. Hydrol. Hydromechanics 61:61–176. https://doi.org/10.2478/johh-2013-0022
Hlavčová K, Kalaš M, Kohnová S, Szolgay J, Danihlík R (2004) Modelling of monthly potential evapotranspiration and runoff in the Hron Basin. J. Hydrol. Hydromech 52:255–266
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2:359–366. https://doi.org/10.1016/0893-6080(89)90020-8
Jones JW, Ritchie JT (1990) Crop growth models. In: Hoffman GJ, Howel TA, Solomon KH (eds) Management of Farm Irrigation System, pp 63–89
Kaya YZ, Mamak M, Unes F (2016a) Evapotranspiration prediction using M5T data mining method. Int J Adv Eng Res Sci. 3. https://doi.org/10.22161/ijaers/3.12.40
Kaya YZ, Üneş F, Mamak M (2016b) Estimating evapotranspiration using adaptive neuro-fuzzy inference system and Hargreaves-Samani method. In: Book of abstracts of the International Conference on Engineering and Natural Sciences (ICENS) 2016. Sarajevo, pp 1983–1989
Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J. Hydrol. 351:299–317. https://doi.org/10.1016/j.jhydrol.2007.12.014
Kişi Ö (2004) River flow modeling using artificial neural networks. J. Hydrol. Eng. 9:60–63. https://doi.org/10.1061/(ASCE)1084-0699(2004)9:1(60)
Kisi O (2005) Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol Sci J 50. https://doi.org/10.1623/hysj.2005.50.4.683
Kisi O (2008) The potential of different ANN techniques in evapotranspiration modelling. Hydrol. Process. 22:2449–2460. https://doi.org/10.1002/hyp.6837
Kisi O (2015) Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. J. Hydrol. 528:312–320. https://doi.org/10.1016/j.jhydrol.2015.06.052
Kişi O, Çimen M (2009) Evapotranspiration modelling using support vector machines / Modélisation de l'évapotranspiration à l'aide de ‘support vector machines’. Hydrol. Sci. J. 54:918–928. https://doi.org/10.1623/hysj.54.5.918
Kisi O, Zounemat-Kermani M (2014) Comparison of two different adaptive neuro-fuzzy inference systems in modelling daily reference evapotranspiration. Water Resour. Manag. 28:2655–2675. https://doi.org/10.1007/s11269-014-0632-0
Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural network. J. Irrig. Drain. Eng. 128:224–233. https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224)
Kumar M, Raghuwanshi NS, Singh R (2011) Artificial neural networks approach in evapotranspiration modeling: a review. Irrig. Sci. 29:11–25. https://doi.org/10.1007/s00271-010-0230-8
Landeras G, Ortiz-Barredo A, López 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). Agric. Water Manag. 95:553–565. https://doi.org/10.1016/j.agwat.2007.12.011
Mirás-Avalos JM, Rubio-Asensio JS, Ramírez-Cuesta JM, Maestre-Valero JF, Intrigliolo DS (2019) Irrigation-advisor-a decision support system for irrigation of vegetable crops. Water (Switzerland) 11. https://doi.org/10.3390/w11112245
Pal M, Deswal S (2009) M5 model tree based modelling of reference evapotranspiration. Hydrol. Process. 23:1437–1443. https://doi.org/10.1002/hyp.7266
Parajka J, Szolgay J, Meszaros I, Kostka Z (2004) Grid-based mapping of the long-term mean annual potential and actual evapotranspiration in upper Hron River basin. In J Hydrol Hydromech, ÚH SAV, no. 4, 2004, 239–254
Rahimikhoob A (2014) Comparison between M5 model tree and neural networks for estimating reference evapotranspiration in an arid environment. Water Resour. Manag. 28:657–669. https://doi.org/10.1007/s11269-013-0506-x
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumenhart DE, McCelland JL (eds) Parallel distributed processing: explorations in the microstructure of cognition. MIT Press, Cambridge, pp 318–362
SHMI, 2015. Climate atlas of Slovakia.
Tasar B, Üneş F, Demirci M, Kaya YZ (2018) Yapay sinir ağları yöntemi kullanılarak buharlaşma miktarı tahmini. DÜMF Mühendislik Derg. 91(1):543–551
Toprak ZF, Cigizoglu HK (2008) Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods. Hydrol. Process. 22:4106–4129. https://doi.org/10.1002/hyp.7012
Traore S, Wang YM, Kerh T (2010) Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone. Agric. Water Manag. 97:707–714. https://doi.org/10.1016/j.agwat.2010.01.002
Turc L (1961) Evaluation des besoins en eau d’irrigation, évapotranspiration potentielle, formulation simplifié et mise à jour. Ann. Agron. 12:13–49
Üneş F, Demirci M, Kişi Ö (2015) Prediction of millers ferry dam reservoir level in USA using artificial neural network. Period. Polytech. Civ. Eng. 59:309–318. https://doi.org/10.3311/PPci.7379
Üneş F, Doğan S, Tasar B, Kaya YZ, Demirci M (2018) The evaluation and comparison of daily reference evapotranspiration with ANN and empirical methods. Nat Eng Sci 3:54–64
Yihdego Y, Webb JA (2018) Comparison of evaporation rate on open water bodies: energy balance estimate versus measured pan. J Water Clim Chang 9:9–111. https://doi.org/10.2166/wcc.2017.139
Zanetti SS, Sousa EF, Oliveira VPS, Almeida FT, Bernardo S (2007) Estimating evapotranspiration using artificial neural network and minimum climatological data. J Irrig Drain Eng. 133:83–89. https://doi.org/10.1061/(ASCE)0733-9437(2007)133:2(83)
Acknowledgments
The authors would like to thank the General Directorate for State Hydraulic Works (DSI) for sharing their measurements.
Funding
This work was supported by the project of the Ministry of Education of the Slovak Republic VEGA 1/0308/20: Mitigation of hydrological hazards—floods and droughts—by exploring extreme hydroclimatic phenomena in river basins and the project of the Slovak Research and Development Agency APVV-17-0549: Research of knowledge and virtual technologies supporting intelligent design and implementation of buildings with emphasis on their economic efficiency and sustainability.
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Conceptualization, F.Ü. and B.T.; compiling data, Y.Z.K.; formal analysis, M.D.; funding acquisition, M.D.; investigation, H.V.; methodology, H.V.; project administration, M.Z.; resources, M.D.; software, Y.Z.K.; supervision, F.Ü., B.T., M.Z., and F.Ü.; validation, F.Ü.; visualization, Y.Z.K.; writing—original draft preparation, Y.Z.K. and H.V.; writing—review and editing, M.Z.
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Kaya, Y.Z., Zelenakova, M., Üneş, F. et al. Estimation of daily evapotranspiration in Košice City (Slovakia) using several soft computing techniques. Theor Appl Climatol 144, 287–298 (2021). https://doi.org/10.1007/s00704-021-03525-z
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DOI: https://doi.org/10.1007/s00704-021-03525-z