References
- Abdel-Fattah, M.K., Kotb Abd-Elmabod, S., Zhang, Z. and Merwad, A.R.M.A., 2023. “Exploring the Applicability of Regression Models and Artificial Neural Networks for Calculating Reference Evapotranspiration in Arid Regions”, Sustainability (Switzerland), Multidisciplinary Digital Publishing Institute (MDPI), Vol. 15 No. 21, doi: 10.3390/su152115494.
- Abdullah, S.S., Malek, M.A., Abdullah, N.S., Kisi, O. and Yap, K.S., 2015. “Extreme Learning Machines: A new approach for prediction of reference evapotranspiration”, Journal of Hydrology, Elsevier, Vol. 527, pp. 184–195, doi: 10.1016/j.jhydrol.2015.04.073.
- Adeloye, A.J., Rustum, R. and Kariyama, I.D., 2012. “Neural computing modeling of the reference crop evapotranspiration”, Environmental Modelling and Software, Vol. 29 No. 1, pp. 61–73, doi: 10.1016/j.envsoft.2011.10.012.
- Allen, R.G. and Food and Agriculture Organization of the United Nations. 1998. Crop Evapotranspiration : Guidelines for Computing Crop Water Requirements, Food and Agriculture Organization of the United Nations.
- Bakhtiari, B., Mohebbi-Dehaghani, A., Qaderi, K., 2022. “Comparative analysis of data-driven methods for daily reference evapotranspiration estimation of Southern Caspian Sea”, Meteorological Applications, Vol. 29 No. 4, pp. 1–16, doi: 10.1002/met.2091.
- Bayram, S. and Çıtakoğlu, H. 2023. Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods. Environmental Monitoring and Assessment, Springer International Publishing, Vol. 195 No. 1, doi: 10.1007/s10661-022-10662-z.
- Benrabah, S., Benrabah, S., Bousnoubra, H., Kherici, N., Cote, M., 2013. Caractérisation de La Qualité Des Eaux de l’oued Kebir Ouest (Nord Est Algérien), Rev. Sci. Technol., Synthèse, Vol. 26.
- Bidabadi, M., Mashal, M., Azadegan, B., Bidabadi, M., 2024. Predictive Modeling of ETO Across Iranian Climates: An ANN and Hybrid Approach”, Papers.Ssrn.Com, pp. 0–22.
- Boser, B.E., Guyon, I.M., Vapnik, V.N., 1992. Training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory. pp. 144–152, doi: 10.1145/130385.130401.
- Chia, M.Y., Huang, Y.F., Koo, C.H., 2020. Support vector machine enhanced empirical reference evapotranspiration estimation with limited meteorological parameters. Computers and Electronics in Agriculture, Elsevier B.V., Vol. 175, doi: 10.1016/j.compag.2020.105577.
- Citakoglu, H., Cobaner, M., Haktanir, T., Kisi, O., 2014. “Estimation of Monthly Mean Reference Evapotranspiration in Turkey”, Water Resources Management, Vol. 28 No. 1, pp. 99–113, doi: 10.1007/s11269-013-0474-1.
- Cobaner, M., Citakoǧlu, H., Haktanir, T., Kisi, O., 2017. Modifying Hargreaves-Samani equation with meteorological variables for estimation of reference evapotranspiration in Turkey. Hydrology Research, Vol. 48 No. 2, pp. 480–497, doi: 10.2166/nh.2016.217.
- Daifallah, T., 2017. Ressources en eau et leurs utilisations dans le bassin versant de l’Oued Kébir Ouest (Nord Est algérien). Thèse de doctorat en science, Université Badji Mokhtar Annaba, 170 pages.
- Dimitriadou, S., Nikolakopoulos, K.G., 2022. Artificial Neural Networks for the Prediction of the Reference Evapotranspiration of the Peloponnese Peninsula, Greece. Water (Switzerland), MDPI, Vol. 14 No. 13, doi: 10.3390/w14132027.
- Droogers, P., Allen, R.G., 2002. Estimating Reference Evapotranspiration under Inaccurate Data Conditions, Irrigation and Drainage Systems, Vol. 16.
- El-Magd, A.A., Baraka, S.M., Eid, S.F.M., 2023. Using artificial neural networks to predict the reference evapotranspiration. Journal of Water and Land Development, Polish Academy of Sciences Publishing House, Vol. 57, pp. 1–8, doi: 10.24425/jwld.2023.143768.
- Fan, J., Yue, W., Wu, L., Zhang, F., Cai, H., Wang, X., Lu, X., Xiang, Y., 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. Agricultural and Forest Meteorology, Elsevier B.V., Vol. 263, pp. 225–241, doi: 10.1016/j.agrformet.2018.08.019.
- Fang, S.L., Lin, Y.S., Chang, S.C., Chang, Y.L., Tsai, B.Y., Kuo, B.J., 2024. Using Artificial Intelligence Algorithms to Estimate and Short-Term Forecast the Daily Reference Evapotranspiration with Limited Meteorological Variables. Agriculture (Switzerland), Multidisciplinary Digital Publishing Institute (MDPI), Vol. 14 No. 4, doi: 10.3390/agriculture14040510.
- Ferreira, L.B., da Cunha, F.F., de Oliveira, R.A., Fernandes Filho, E.I., 2019. Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – A new approach. Journal of Hydrology, Elsevier B.V., Vol. 572, pp. 556–570, doi: 10.1016/j.jhydrol.2019.03.028.
- Gocic, M., Trajkovic, S., 2010. Software for estimating reference evapotranspiration using limited weather data. Computers and Electronics in Agriculture, Vol. 71 No. 2, pp. 158–162, doi: 10.1016/j.compag.2010.01.003.
- Gong, D., Hao, W., Gao, L., Feng, Y., Cui, N., 2021. Extreme learning machine for reference crop evapotranspiration estimation: Model optimization and spatiotemporal assessment across different climates in China. Computers and Electronics in Agriculture, Elsevier B.V., Vol. 187, doi: 10.1016/j.compag.2021.106294.
- Grant Exner-Kittridge, M., Asce, M., Rains, M.C., 2010. Case Study on the Accuracy and Cost/Effectiveness in Simulating Reference Evapotranspiration in West-Central Florida. Journal of Hydrologic Engineering, Vol. 15 No. 9, pp. 696–703, doi: 10.1061/ASCEHE.1943-5584.0000239.
- Holzkämper, A., Calanca, P., Fuhrer, J., 2013. Identifying climatic limitations to grain maize yield potentials using a suitability evaluation approach. Agricultural and Forest Meteorology, Vol. 168, pp. 149–159, doi: 10.1016/j.agrformet.2012.09.004.
- Hou, L. gong, Zou, S. bing, Xiao, H. lang and Yang, Y. gang., 2013. Sensitivity of the reference evapotranspiration to key climatic variables during the growing season in the Ejina oasis northwest China. SpringerPlus, SpringerOpen, Vol. 2 No. 1, doi: 10.1186/2193-1801-2-S1-S4.
- Karahan, H., Çetin, M., Can, M.E., Alsenjar, O., 2024. “Developing a New ANN Model to Estimate Daily Actual Evapotranspiration Using Limited Climatic Data and Remote Sensing Techniques. doi: 10.20944/preprints202402.0617.v1.
- Karimi, P., Bastiaanssen, W.G.M., 2015. Spatial evapotranspiration, rainfall and land use data in water accounting - Part 1: Review of the accuracy of the remote sensing data. Hydrology and Earth System Sciences, Copernicus GmbH, 28 January, doi: 10.5194/hess-19-507-2015.
- Katipoğlu, O.M., Keblouti, M., 2024. Comparative analysis of data-driven models and signal processing techniques in the monthly maximum daily precipitation prediction of El Kerma station Northeast of Algeria. Soft Computing, doi: 10.1007/s00500-024-09860-3.
- Kisi, O., 2016. Modeling reference evapotranspiration using three different heuristic regression approaches. Agricultural Water Management, Elsevier B.V., Vol. 169, pp. 162–172, doi: 10.1016/j.agwat.2016.02.026.
- Laaboudi, A., Mouhouche, B., Draoui, B., 2012. Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions. International Journal of Biometeorology, Vol. 56 No. 5, pp. 831–841, doi: 10.1007/s00484-011-0485-7.
- Landeras, G., Ortiz-Barredo, A., López, J.J., 2008. Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agricultural Water Management, Vol. 95 No. 5, pp. 553–565, doi: 10.1016/j.agwat.2007.12.011.
- Majhi, B., Naidu, D., 2021. Differential evolution based radial basis function neural network model for reference evapotranspiration estimation. SN Applied Sciences, Springer International Publishing, Vol. 3 No. 1, pp. 1–19, doi: 10.1007/s42452-020-04069-z.
- Moazenzadeh, R., Mohammadi, B., Shamshirband, S., Chau, K.W., 2018. Coupling a firefly algorithm with support vector regression to predict evaporation in northern Iran. Engineering Applications of Computational Fluid Mechanics, Taylor and Francis Ltd., Vol. 12 No. 1, pp. 584–597, doi: 10.1080/19942060.2018.1482476.
- Sabanci, D., Yurekli, K., Comert, M.M., Kilicarslan, S., Erdogan, M., 2023. Predicting reference evapotranspiration based on hydro-climatic variables: comparison of different machine learning models. Hydrological Sciences Journal, Taylor and Francis Ltd., Vol. 68 No. 7, pp. 1050–1063, doi: 10.1080/02626667.2023.2203824.
- Sattari, M.T., Pal, M., Yürekli, K., Ünlükara, A., 2013. M5 model trees and neural network based modelling of ET0 in Ankara, Turkey. Turkish Journal of Engineering and Environmental Sciences, Vol. 37 No. 2, pp. 211–219, doi: 10.3906/muh-1212-5.
- Shrestha, N.K., Shukla, S., 2015. Support vector machine based modeling of evapotranspiration using hydro-climatic variables in a sub-tropical environment. Agricultural and Forest Meteorology, Elsevier, Vol. 200, pp. 172–184, doi: 10.1016/j.agrformet.2014.09.025.
- Skhiri, A., Ferhi, A., Bousselmi, A., Khlifi, S. Mattar, M.A., 2024. Artificial Neural Network for Forecasting Reference Evapotranspiration in Semi-Arid Bioclimatic Regions. Water (Switzerland), Vol. 16 No. 4, doi: 10.3390/w16040602.
- Tabari, H., 2010. Evaluation of reference crop evapotranspiration equations in various climates. Water Resources Management, Vol. 24 No. 10, pp. 2311–2337, doi: 10.1007/s11269-009-9553-8.
- Tabari, H., Aeini, A., Talaee, P.H., Some’e, B.S., 2012. Spatial distribution and temporal variation of reference evapotranspiration in arid and semi-arid regions of Iran. Hydrological Processes, Vol. 26 No. 4, pp. 500–512, doi: 10.1002/hyp.8146.
- Tejada, A.T., Ella, V.B., Lampayan, R.M., Reaño, C.E., 2022. Modeling Reference Crop Evapotranspiration Using Support Vector Machine (SVM) and Extreme Learning Machine (ELM) in Region IV-A, Philippines. Water (Switzerland), MDPI, Vol. 14 No. 5, doi: 10.3390/w14050754.
- Trajkovic, S., Kolakovic, S., 2009. Estimating Reference Evapotranspiration Using Limited Weather Data, doi: 10.1061/ASCEIR.1943-4774.0000094.
- Traore, S., Wang, Y.M., Kerh, T., 2010. Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone. Agricultural Water Management, Vol. 97 No. 5, pp. 707–714, doi: 10.1016/j.agwat.2010.01.002.
- Triana-Madrid, J.C., Ocampo-Marulanda, C., Carvajal-Escobar, Y., Torres-López, W.A., Triana, J., Canchala, T., 2023. Estimation of Monthly Reference Evapotranspiration with Scarce Information Using Machine Learning in Southwestern Colombia. Meteorologica, Universidad Nacional de La Plata, Vol. 48 No. 2, p. 024, doi: 10.24215/1850468xe024.
- Weisrock, A. 2002. Cosandey C., Robinson M., 2000, Hydrologie continentale. Revue Géographique de l’Est, OpenEdition, Vol. 42 No. 4, doi: 10.4000/rge.2715.
- Wen, X., Si, J., He, Z., Wu, J., Shao, H., Yu, H., 2015. Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration with Limited Climatic Data in Extreme Arid Regions. Water Resources Management, Kluwer Academic Publishers, Vol. 29 No. 9, pp. 3195–3209, doi: 10.1007/s11269-015-0990-2.
- Wu, L., Zhou, H., Ma, X., Fan, J., Zhang, F., 2019. Daily reference evapotranspiration prediction based on hybridized extreme learning machine model with bio-inspired optimization algorithms: Application in contrasting climates of China. Journal of Hydrology, Elsevier B.V., Vol. 577, doi: 10.1016/j.jhydrol.2019.123960.
- Zouzou, Y., Citakoglu, H., 2023. “General and regional cross-station assessment of machine learning models for estimating reference evapotranspiration”, Acta Geophysica, Springer International Publishing, Vol. 71 No. 2, pp. 927–947, doi: 10.1007/s11600-022-00939-9