Adaptive Neuro-Fuzzy inference system (ANFIS) based models for estimation of reference evapotranspiration (ET0)

Authors

  • MAHADEVA M. Department of Civil Engineering, RNS Institute of Technology, Bangaluru and Department of Civil Engineering, University of Visvesvaraya College of Engineering, Bengaluru, Karnataka, India
  • SRIRAM A. V. Department of Civil Engineering, University of Visvesvaraya College of Engineering, Bengaluru, Karnataka, India

DOI:

https://doi.org/10.54386/jam.v27i3.3008

Keywords:

Reference evapotranspiration, Machine learning, ANN, Neuro-Fuzzy inference system (ANFIS)

References

Adnana, N., Hamid, A. T. A., and Wahab, A. F. (2021). ANFIS-WCAMFO hybrid model for monthly ET₀ estimation in data-scarce environments. Water Resour. Manage., 35(2): 543–558.

Allen, R. K., Pereira, L. S., Raes, D. and Smith, M. (1998). Crop evapotranspiration guideline for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56. United Nations Food and Agricultural Organization, Rome

Amit Bijlwan, Shweta Pokhariyal, Rajeev Ranjan, R.K Singh and Ankita Jha (2024). Machine learning methods for estimating reference evapotranspiration, J. Agrometeorol., 26(1): 63 – 68. https://doi.org/10.54386/jam.v26i1.2462

Dogan, E. (2009). Daily grass crop reference evapotranspiration estimation using ANFIS. J. Irrig. Drain. Engg., 135(3): 330–337.

Gavili, A., Mousavi, S. J., and Alizadeh, A. (2017). Comparison of ANFIS and multiple linear regression models for estimation of daily reference evapotranspiration in a semi-arid climate. J. Irrig. Drain. Engg., 143(6): 04017014.

Gloria I. Ezenne, Nkpouto U. Eyibio, Jane L. Tanner, Felix U. Asoiro and Sunday E. Obalum (2023). An overview of uncertainties in evapotranspiration estimation techniques, J. Agrometeorol., 25 (1): 173 – 182. https://doi.org/10.54386/jam.v25i1.2014

Goyal, M. K., Ojha, C. S. P., and Singh, R. D. (2014). Modeling of pan evaporation using ANN, ANFIS, and GEP models. J. Hydrol. Sci., 59(1): 116–130.

Keskin, M. E., and Terzi, O. (2009). Artificial neural network and adaptive neuro-fuzzy inference system for predicting daily pan evaporation. J. Hydrol., 372(1-4): 104–112.

Mosavi, A., and Mohammad, R. (2019). A hybrid ANFIS–PSO–PCA model for ET₀ prediction: Enhancing accuracy through optimization and feature reduction. Environ. Earth Sci., 78(1): 1–14.

Rezaabad, M. A., Moosavi, V., and Kisi, O. (2020). Hybrid ANFIS models for estimation of ET₀ using optimization algorithms. Agric. Water Manag., 231: 106003.

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Published

01-09-2025

How to Cite

M., M., & SRIRAM A. V. (2025). Adaptive Neuro-Fuzzy inference system (ANFIS) based models for estimation of reference evapotranspiration (ET0). Journal of Agrometeorology, 27(3), 381–384. https://doi.org/10.54386/jam.v27i3.3008

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Short Communication

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