Abstract
This study was aimed at investigating the potentials of Artificial Neural Network (ANN), Support Vector Regression (SVR) and Multiple Linear Regression (MLR) techniques for the estimation of reference evapotranspiration (ET0) in a semiarid station of Nigeria. To do so, 34 years daily monthly average data including maximum, minimum and mean temperatures (Tmax, Tmin, and Tmean), relative humidity (RH) and wind speed (U2) were used as input parameters. Three models were developed from each technique using three different input combinations. FAO Penman Monteith method was used as the basis upon which the performances of the models were assessed. The results revealed that models developed using Tmin, Tmax and U2 produced better performance. The results also depicted that with the unique capability of each technique, different results would be obtained, both ANN and SVR models could lead to efficient and reliable results, but MLR model could not produce reliable performance due to its inability to deal with nonlinear aspect of ET0.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Odhiambo, L.O., Yoder, R.E., Yoder, D.C.: Estimation of reference crop evapotranspiration using fuzzy state models. Trans. ASAE 44, 543–550 (2001)
Allen, R.G., Pereira, L.S., Raes, D., Smith, M.: Crop evapotranspiration-guidelines for computing crop water requirements. FAO Irrigation and drainage paper 56. FAO, Rome, 300(9) (1998)
Ladlani, I., Houichi, L., Djemili, L., Heddam, S., Belouz, K.: Estimation of daily reference evapotranspiration (ET0) in the North of Algeria using adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) models: a comparative study. Arab. J. Sci. Eng. 39, 5959–5969 (2014). https://doi.org/10.1007/s13369-014-1151-2
Abdullahi, J., Elkiran, G.: Prediction of the future impact of climate change on reference evapotranspiration in Cyprus using artificial neural network. In: 9th International Conference on Theory and Application of Soft Computing, Computing with Words and Perception, pp. 276–283. Elsevier, London (2017). https://doi.org/10.1016/j.procs.2017.11.239
Antonopoulos, V.Z., Antonopoulos, A.V.: Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables. Comput. Electron. Agric. 132, 86–96 (2017). https://doi.org/10.1016/j.compag.2016.11.011
Yin, Z., Wen, X., Feng, Q., He, Z., Zou, S., Yang, L.: Integrating genetic algorithm and support vector machine for modeling daily reference evapotranspiration in a semi-arid mountain area. Hydrol. Res. 48, 1177–1191 (2017). https://doi.org/10.2166/nh.2016.205
Fan, J., Yue, W., Wu, L., Zhang, F., Cai, H., Wang, X., Xiang, Y.: Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agric. For. Meteorol. 263, 225–241 (2018). https://doi.org/10.1016/j.agrformet.2018.08.019
Arku, A.Y., Musa, S.M., Mofoke, A.L.E.: Determination of water requirement and irrigation timing for Amaranthus hybridus in Maiduguri metropolis, north-eastern Nigeria. In: Fourth International Conference on Sustainable Irrigation, pp. 279–289. WIT Press, England (2012). https://doi.org/10.2495/si120241
Elkiran, G., Nourani, V., Abba, S.I., Abdullahi, J.: Artificial intelligence-based approaches for multi-station modelling of dissolve oxygen in river. Global J. Environ. Sci. Manag. 4, 439–450 (2018). https://doi.org/10.22034/gjesm.2018.04.005
Legates, D.R., McCabe Jr., G.J.: Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35, 233–241 (1999)
Abdullahi, J., Elkiran, G., Nourani, V.: Application of Artificial Neural Network to predict reference evapotranspiration in Famagusta, North Cyprus. In: 11th International Scientific Conference on Production Engineering Development and Modernization of Production, pp. 549–554 (2017)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)
Nourani, V., Elkiran, G., Abba, S.I.: Wastewater treatment plant performance analysis using artificial intelligence–an ensemble approach. Water Sci. Technol. 78, 2064–2076 (2018). https://doi.org/10.2166/wst.2018.477
Nourani, V., Elkiran, G., Abdullahi, J., Tahsin, A.: Multi-region modeling of daily global solar radiation with artificial intelligence ensemble. Nat. Resour. Res. 28, 1217–1238 (2019). https://doi.org/10.1007/s11053-018-09450-9
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Abdullahi, J., Elkiran, G., Nourani, V. (2020). Artificial Intelligence Based and Linear Conventional Techniques for Reference Evapotranspiration Modeling. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham. https://doi.org/10.1007/978-3-030-35249-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-35249-3_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35248-6
Online ISBN: 978-3-030-35249-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)