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

Authors

  • 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

DOI:

https://doi.org/10.13128/ijam-971

Keywords:

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

Abstract

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.

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Published

2021-01-25

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. https://doi.org/10.13128/ijam-971

Issue

No. 2 (2020)

Section

RESEARCH ARTICLES

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