Improving potential evapotranspiration prediction accuracy using advanced machine learning models: a case study in Ahvaz, Iran

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Abstract

Accurate forecasting of daily potential evapotranspiration (ET_o) is vital for efficient irrigation scheduling, sustainable water resource management, and optimal crop yield, especially in arid regions. In this study, we present a novel, large-scale comparison of three advanced machine learning models, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Gene Expression Programming (GEP), for ET_o prediction at the Ahvaz synoptic station in Iran. The work is distinctive in using one of the longest ET_o datasets available (1979–2023; 16,084 records), incorporating a sensitivity analysis for input selection, and applying the Developed Discrepancy Ratio (DDR) as an advanced performance metric. The ANN model (MLP 4-9-1 architecture) demonstrated the highest prediction accuracy, achieving an R² of 0.9806, RMSE of 0.4122, and DDRmax of 3.27 in the training phase, and an R² of 0.9779, RMSE of 0.4327, and DDR_max of 3.22 during validation. In comparison, SVM and GEP models showed lower accuracy across all phases. These results highlight the superior capability of the ANN model for ET_o forecasting and its potential as a reliable tool for irrigation planning and water resource management in arid regions like Ahvaz.

Keywords

climate / artificial intelligence / dry region / water cycle / performance assessment

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References

[1] Braddock RD, Kremmer ML, Sanzogni L (1998) Feed-forward artificial neural network model for forecasting rainfall run-off. Environmetrics 9: 419-432. https://doi.org/10.1002/(SICI)1099-095X(199807/08)9:4<419::AID-ENV312>3.0.CO;2-D

[2] Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J hydrol 330: 621-640. https://doi.org/10.1016/j.jhydrol.2006.04.030

[3] Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I-A discussion of principles. J hydrol 10: 282-290. https://doi.org/10.1016/0022-1694(70)90255-6

[4] Zhang G, Bateni SM, Jun C, Khoshkam H, Band SS, Mosavi A (2022) Feasibility of random forest and multivariate adaptive regression splines for predicting long-term mean monthly dew point temperature. Front Environ Sci 10: 826165. https://doi.org/10.3389/fenvs.2022.826165

[5] Elbeltagi A, Kushwaha NL, Rajput J, et al. (2022) Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions. Stoch Environ Res Risk Assess 36: 3311-3334. https://doi.org/10.1007/s00477-022-02196-0

[6] Dimitriadou S, Nikolakopoulos KG (2022) Artificial neural networks for the prediction of the reference evapotranspiration of the Peloponnese Peninsula, Greece. Water 14: 2027. https://doi.org/10.3390/w14132027

[7] Malik A, Jamei M, Ali M, et al. (2022) Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection. Agr Water Manage 272: 107812. https://doi.org/10.1016/j.agwat.2022.107812

[8] Aghelpour P, Norooz-Valashedi R (2022) Predicting daily reference evapotranspiration rates in a humid region, comparison of seven various data-based predictor models. Stoch Environ Res Risk Ass 36: 4133-4155. https://doi.org/10.1007/s00477-022-02249-4

[9] Achite M, Jehanzaib M, Sattari MT, et al. (2022) Modern techniques to modeling reference evapotranspiration in a semiarid area based on ANN and GEP models. Water 14: 1210. https://doi.org/10.3390/w14081210

[10] Sanikhani H, Deo RC, Samui P, et al. (2018) Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors. Comput Electron Agr 152: 242-260. https://doi.org/10.1016/j.compag.2018.07.008

[11] Bellido-Jiménez JA, Gualda JE, García-Marín AP (2021) Assessing new intra-daily temperature-based machine learning models to outperform solar radiation predictions in different conditions. Appl Energ 298: 117211. https://doi.org/10.1016/j.apenergy.2021.117211

[12] Yakut E, Süzülmüş S (2020) Modelling monthly mean air temperature using artificial neural network, adaptive neuro-fuzzy inference system and support vector regression methods: A case of study for Turkey. Network Comp Neural 31: 1-36. https://doi.org/10.1080/0954898X.2020.1759833

[13] Citakoglu H, Babayigit B, Haktanir NA (2020) Solar radiation prediction using multi-gene genetic programming approach. Theor Appl Climatol 142: 885-897. DOI: 10.1007/s00704-020-03356-4

[14] Biazar SM, Rahmani V, Isazadeh M, et al. (2020) New input selection procedure for machine learning methods in estimating daily global solar radiation. Arab J Geosci 13: 1-17. https://doi.org/10.1007/s12517-020-05437-0

[15] Raza A, Shoaib M, Faiz MA, et al. (2020) Comparative assessment of reference evapotranspiration estimation using conventional method and machine learning algorithms in four climatic regions. Pure Appl Geophys 177: 4479-4508. https://doi.org/10.1007/s00024-020-02473-5

[16] Lu X, Fan J, Wu L, et al. (2020) Forecasting multi-step ahead monthly reference evapotranspiration using hybrid extreme gradient boosting with grey wolf optimization algorithm. Comp Model Eng Sci 125: 699-723. https://doi.org/10.32604/cmes.2020.011004

[17] Maroufpoor S, Sanikhani H, Kisi O, et al. (2019) Long-term modelling of wind speeds using six different heuristic artificial intelligence approaches. Int J Climatol 39: 3543-3557. https://doi.org/10.1002/joc.6037

[18] Jahani B, Mohammadi B (2019) A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran. Theor Appl Climatol 137: 1257-1269. https://doi.org/10.1007/s00704-018-2666-3

[19] Kiafar H, Babazadeh H, Marti P, et al. (2017) Evaluating the generalizability of GEP models for estimating reference evapotranspiration in distant humid and arid locations. Theor Appl Climatol 130: 377-389. https://doi.org/10.1007/s00704-016-1888-5

[20] Mehdizadeh S, Behmanesh J, Khalili K (2017) Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Comput Electron Agr 139: 103-114. https://doi.org/10.1016/j.compag.2017.05.002

[21] Tezel G, Buyukyildiz M (2016) Monthly evaporation forecasting using artificial neural networks and support vector machines. Theor Appl Climatol 124: 69-80. https://doi.org/10.1007/s00704-015-1392-3

[22] Sharifi SS, Rezaverdinejad V, Nourani V (2016) Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches. J Atmos Sol-Terr Phys 149: 131-145. https://doi.org/10.1016/j.jastp.2016.10.008

[23] Martí P, González-Altozano P, López-Urrea R, et al. (2015) Modeling reference evapotranspiration with calculated targets. Assessment and implications. Agr Water Manage 149: 81-90. https://doi.org/10.1016/j.agwat.2014.10.028

[24] Kisi O, Sanikhani H, Zounemat-Kermani M, et al. (2015) Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Comput Electron Agr 115: 66-77. https://doi.org/10.1016/j.compag.2015.04.015

[25] Shiri J, Marti P, Kisi O, et al. (2013) Evaluation of different data management scenarios for estimating daily reference evapotranspiration. Hydrol Res 44: 1058-1070. https://doi.org/10.2166/nh.2013.154

[26] Traore S, Guven A (2012) Regional-specific numerical models of evapotranspiration using gene-expression programming interface in sahel. Water Resour Manag 26: 4367-4380. https://doi.org/10.1007/s11269-012-0149-3

[27] Izadifar Z, Elshorbagy A (2010) Prediction of hourly actual evapotranspiration using neural networks, genetic programming, and statistical models. Hydrol Process 24: 3413-3425. https://doi.org/10.1002/hyp.7771

[28] Kisi O, Guven A (2010) Evapotranspiration modeling using linear genetic programming technique. J Irrig Drain Eng 136: 715-723. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000244

[29] Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J Hydrol 351. https://doi.org/10.1016/j.jhydrol.2007.12.014

[30] Fuladipanah M, Azamathulla HM, Kisi O, et al. (2024) Quantitative forecasting of bed sediment load in river engineering: an investigation into machine learning methodologies for complex phenomena. Water Supply 24: 585-600. https://doi.org/10.2166/ws.2024.017

[31] Sajindra H, Abekoon T, Wimalasiri EM, et al. (2023) An Artificial Neural Network for Predicting Groundnut Yield Using Climatic Data. Agri Engineering 5 1713-1736. https://doi.org/10.3390/agriengineering5040106

[32] Jayathilake T, Sarukkalige R, Hoshino Y, et al. (2022) Wetland water level prediction using artificial neural networks-A case study in the Colombo flood detention area, Sri Lanka. Climate 11. https://doi.org/10.3390/cli11010001

[33] Azamathulla HM, Rathnayake U, Shatnawi A (2018) Gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia. Appl Water Sci 8: 1-7. https://doi.org/10.1007/s13201-018-0831-6

[34] Leon LP, Azamathulla HM, Felix P, et al. (2023) Prediction of stiffness modulus of bituminous mixtures using the applications of multi-expression programming and gene expression programming. Road Mater Pavement Des 24: 2192-2208. https://doi.org/10.1080/14680629.2022.2126383

[35] Fuladipanah M, Hazi MA, Kisi O (2023) An in-depth comparative analysis of data-driven and classic regression models for scour depth prediction around cylindrical bridge piers. Appl Water Sci 13: 231. https://doi.org/10.1007/s13201-023-02022-0

[36] Gharehbaghi A, Ghasemlounia R, Afaridegan E, et al. (2023) A comparison of artificial intelligence approaches in predicting discharge coefficient of streamlined weirs. J Hydroinform 25: 1513-1530. https://doi.org/10.2166/hydro.2023.063

[37] Azamathulla HM (2012) Gene-expression programming to predict scour at a bridge abutment. J Hydroinform 14: 324-331. https://doi.org/10.2166/hydro.2011.135

[38] Cortes C, Vapnik V (1995) Support-vector networks. Mach learn 20: 273-297. https://doi.org/10.1007/BF00994018

[39] Kumar V, Azamathulla HM, Sharma KV, et al. (2023) The state of the art in deep learning applications, challenges, and future prospects: A comprehensive review of flood forecasting and management. Sustainability 15: 10543. https://doi.org/10.3390/su151310543

[40] Rathnayake N, Rathnayake U, Dang TL, et al. (2023) Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka. Plos one 18: e0282847. https://doi.org/10.1371/journal.pone.0282847

[41] Noori R, Khakpour A, Omidvar B, et al. (2010) Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistics. Expert Syst Appl 37: 5856-5862. https://doi.org/10.1016/j.eswa.2010.02.020

[42] Koncar N (1997) Optimization methodologies for direct inverse neurocontrol. (Publication No., SW72BZ) [Doctoral dissertation, London University, Englan].

[43] Malik A, Tikhamarine Y, Al-Ansari N, et al. (2021) Daily pan-evaporation estimation in different agroclimatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test. Eng Appl Comp Fluid Mech 15: 1075-1094. https://doi.org/10.1080/19942060.2021.1942990