Using Neuro-fuzzy and linear models to estimate reference Evapotranspiration in South region of Algeria (A comparative study)


  • Abdelkader Laaboudi National Institute of research in agronomy of Algeria. Experimental station of Adrar, Algeria
  • Abdeldjalil Slama Laboratory of Mathematics, Modeling and Applications (LAMMA), University of Adrar, Algeria



Reference evapotranspiration, arid regions, Adaptive Neuro-Fuzzy Inference System, robust regression, Bayesian regression, Penman-Monteith formula


In order to estimate daily reference evapotranspiration (ETo) in arid region of Algeria, Adaptive Neuro-Fuzzy Inference System (ANFIS) and regression methods as Robust Regression (RR), Bayesian Regression (BR) and Multiple Linear Regression (MLR) techniques were used to develop models based on four explanatory climatic factors: temperature, relative humidity, wind speed and sunshine duration. These factors have been used as inputs, and ETo values computed by the Penman-Monteith formula have been used as outputs. Determination coefficient (R²), root mean square error (RMSE), Mean absolute error (MAE), mean absolute relative error (MARE) and Nash-Sutcliffe efficiency coefficient (NSE) were used to evaluate the performance of models developed with different input configurations. We concluded that RR, BR and MLR models were able to successfully estimate ETo, but ANFIS technique seems to be more powerful. Thus, the obtained results by the best ANFIS model, during the test phase are: 0.98, 0.27 (mm/day)², 0.36 (mm/day) and 5.52 % respectively for R, MAE, RMSE and MARE.


Abyaneh H. Z., Nia A. M., Varkeshi M. B., Marofi S., Kisi O. 2011. Performance evaluation of ANN and ANFIS models for estimating garlic crop evapotranspiration. Journal of irrigation and drainage engineering, 137(5): 280-286.
Allen R.G., Pereira L.S., Raes D., Smith M. 1998. Crop evapotranspiration (guidelines focomputing irrigation crop water requirement).Irrigation and drainage paper no. 56. Food and Agriculture Organization. Rome.
Areerachakul S. 2012. Comparison of ANFIS and ANN for Estimation of Biochemical Oxygen Demand Parameter in Surface Water. International Journal of Chemical and Biological Engineering. 6: 286 -290.
Bernard M. and Smith A. F. 2000 . Bayesian Theory. Hoboken, NJ, USA: Wiley..
Braga J., Heuze Y., Chabadel O., Sonan. N. K., Gueramy A. 2005. Non-adult dental age assessment: correspondence analysis and linear regression versus Bayesian predictions. Int J Legal Med. 119: 260–274 DOI 10.1007/s00414-004-0494-8
Demelash N. 2013. Deficit irrigation scheduling for potato production in North Gondar, Ethiopia. African Journal of Agricultural Research. 8(11): 1144-1154.
Doorenbos J., Pruitt W.O. 1977. Guidelines for predicting crop water requirements. Irrigation and drainage.pap. 24. Food and Agriculture Organization, Rome.
Fang W., Huang S., Huang Q., Huang G., Meng E., Luan J. 2018. Reference evapotranspiration forecasting based on local meteorological and global climate information screened by partial mutual information. Journal of Hydrology, 561, 764-779.
Fox J., and Weisberg S. 2002. Robust regression. An R and S-Plus companion to applied regression, 91.
Ghosh J. K., Delampady M., Samanta T. 2007. An introduction to Bayesian analysis: theory and methods. Springer Science & Business Media.
Gocic M. and Trajkovic S. 2014. Drought characterisation based on water surplus variability index. Water resources management, 28(10), 3179-3191.
Grzenda W. 2015. The advantages of Bayesian methods over Classical methods in the context of credible Intervals. Information systems in management. 4 (1): 53-63.
Hunsaker D.J., Fitzgerald G.J, French A.N., Clarke T.R., Ottman M., Jand P.J. 2007. Wheat irrigation management using multispectral crop coefficients: II. Irrigation scheduling performance. grain yield and water use efficiency. Am Soc Agric Biol Eng 50(6):2035–2050.
Jange J. 1993. ANFIS: Adaptive-network-based fuzzy inference system." IEEE transactions on systems. man. and cybernetics. 23(3): 665-685.
Karimaldini F., Teang Shui L., Ahmed Mohamed T., Abdollahi M., Khalili N. 2012. Daily evapotranspiration modeling from limited weather data by using neuro-fuzzy computing technique. Journal of irrigation and drainage engineering, 138(1), 21-34.
Keshtegar B., Kisi O., Arab H. G., Zounemat-Kermani M. 2018. Subset modeling basis ANFIS for prediction of the reference evapotranspiration. Water resources management, 32(3): 1101-1116.
Khoshravesh M., Sefidkouhi M.A.G., Valipour M. 2015. Estimation of reference evapotranspiration using multivariate fractional polynomial, Bayesian regression, and robust regression models in three arid environments. Appl Water Sci. 7(4): 1911–1922.
Kumar P., Kumar D., Jaipaul Tiwari A.K. 2012. Evaporation Estimation Using Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System Techniques. Pakistan Journal of Meteorology. 8 (16): 81-88.
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, 56(5): 831-841.
Ladlani I., Houichi L., Djemil L., Heddam S., Belouz K. 2016. Estimation of Daily Reference Evapotranspiration (ETO) 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 (8).
Latrech B., Ghazouani H., Lasram A., M’hamdi B. D., Mansour M., Boujelben A. 2019 . Assessment of different methods for simulating actual evapotranspiration in a semi-arid environment. Italian Journal of Agrometeorology, (2): 21-34.
Lee K. H., Cho H. Y. 2012. Simple method for estimating pan coefficients: Conversion of pan evaporation to reference evapotranspiration. Journal of irrigation and drainage engineering, 138(1): 98-103.
Malamos N., Barouchasa P.E., Tsirogiannisb I.L., Liopa-Tsakalidia A., Koromilasc Th. 2015. Estimation of monthly FAO Penman-Monteith evapotranspiration in GIS environment, through a geometry independent algorithm Agriculture and Agricultural Science Procedia 4 2015 290 – 299
Meng Li., Ronghao Chu., Abu Reza Md Towfiqul Islam, Shuanghe, Shen. 2018. Reference Evapotranspiration Variation Analysis and Its Approaches Evaluation of 13 Empirical Models in Sub-Humid and Humid Regions: A Case Study of the Huai River Basin, Eastern China .Water 10(4): 493-502
Naidu D. and Majhi B. 2019. Reference evapotranspiration modeling using radial basis function neural network in different agro-climatic zones of Chhattisgarh. Journal of Agrometeorology 21 (3) : 316-326
Patil A. P, and Deka P.C. 2017. Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India. Neural Computing and Applications, 28(2): 275-285.
Peng, L., Li, Y., Feng, H. 2017. The best alternative for estimating reference crop evapotranspiration in different sub-regions of mainland China. Scientific reports, 7(1), 1-19.
Pour-Ali Baba A., Shiri J., Kisi O., Fard A. F., Kim S., Amini R. 2015. Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrology Research, 44(1): 131-146.
Saylan L., Kimura R., Altınbaş N., Çaldağ B., Bakanoğulları F. 2019. Modeling of Surface Conductance over Sunn Hemp by Artificial Neural Network. Italian Journal of Agrometeorology, (3): 37-48.
Shamshirband S., Amirmojahedi M., Gocić M., Akib S., Petković D., Piri J., Trajkovic S. 2016. Estimation of reference evapotranspiration using neural networks and cuckoo search algorithm. Journal of Irrigation and Drainage Engineering, 142(2): 04015044.
Stahel W. 1997. “Robust alternatives to least squares,” Adv. Math. Tools in Metrol. III, Ser. Adv. Math. for Appl. Sci., World Scientific Publishing Company, 45, 118–133.
Tabari H., Kisi O., Ezani A., Talaee, P. H. 2012. SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. Journal of Hydrology, 444: 78-89.
Trajkovic S., and Kolakovic S. 2010. Comparison of simplified pan-based equations for estimating reference evapotranspiration. Journal of irrigation and drainage engineering, 136(2): 137-140.
Tsakiris G., Pangalou D., Vangelis H. 2007. Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water resources management, 21(5): 821-833.
Vicente-Serrano S. M., Beguería S., López-Moreno J. I. 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of climate, 23(7): 1696-1718.
Wable P.S., Jha M.K., Gorantiwar S.D. 2019. Assessing suitability of temperature-based reference evapotranspiration methods for semi-arid basin of Maharashtra. Journal of Agrometeorology 21 (3): 351-356.
Yaseen A H., Amir P.N., Zhen Z., Subhasis G., Sean A.W. 2016. Bayesian Regression and Neuro-Fuzzy Methods Reliability Assessment for Estimating Streamflow. Water, 8, 287 pp15.
Yirga S. A. 2019. Modelling reference evapotranspiration for Megecha catchment by multiple linear regression. Modeling Earth Systems and Environment, 5(2): 471-477.




How to Cite

Laaboudi, A., & Slama, A. (2021). Using Neuro-fuzzy and linear models to estimate reference Evapotranspiration in South region of Algeria (A comparative study). Italian Journal of Agrometeorology, (2), 55-64.