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Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data

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Abstract

Accurate prediction of the reference evapotranspiration (ET0) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinations of the meteorological data for predicting the ET0 in the Beas-Sutlej basin of Himachal Pradesh (India). Four climatic locations in the basin namely, Kullu, Mandi, Bilaspur, and Chaba were selected. The meteorological dataset comprised air temperature (maximum, minimum and mean), relative humidity, solar radiation, and wind speed, recorded daily for a period of 35 years (1984–2019). The datasets from 1984 to 2012 and 2013 to 2019 were utilized for training and testing the models, respectively. The performance of the developed models was evaluated using several statistical indices. For each location, the best performed MLP-ANN model was the one with the complete combination of the meteorological data. The architecture of the best performing model for Kullu, Mandi, Bilaspur, and Chaba was (6–2-4–1), (6–5-4–1), (6–5-4–1), and (6–4-6–1), respectively. It was observed, however, that the performance of other models was also relatively good, given the limited meteorological data utilized in those models. Further, to appreciate the relative predictive ability of the developed models, a comparison was performed with four existing established empirical models. The approach adopted in this study can be effectively utilized by water users and field researchers for modelling and predicting ET0 in data-scarce locations.

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Data availability

Some data, models, or code used during the study are available from the first author and corresponding author by request.

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Funding

The work was supported by National Institute of Technology Hamirpur (India), Shoolini University (India), and Mansoura University (Egypt).

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Authors and Affiliations

  1. Agricultural Engineering Dept, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt

    Ahmed Elbeltagi

  2. Department of Civil Engineering, Shoolini University, Solan, Himachal Pradesh, 173229, India

    Navsal Kumar

  3. Department of Civil Engineering, National Institute of Technology Hamirpur, Himachal Pradesh, 177005, India

    Abhishish Chandel

  4. Department of Biosystems and Agricultural Engineering, Oklahoma State University, Oklahoma, Stillwater, 74078, USA

    Arfan Arshad

  5. AIRCP for Dryland Agriculture, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola, India

    Chaitanya B. Pande

  6. Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh

    Abu Reza Md. Towfiqul Islam

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  1. Ahmed Elbeltagi
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  2. Navsal Kumar
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Contributions

Navsal Kumar and Ahmed Elbeltagi conceived the idea of the paper. Ahmed Elbeltagi and Chaitanya B. Pande performed the modelling simulations. Ahmed Elbeltagi compiled the results. Navsal Kumar, Ahmed Elbeltagi and Abhishish Chandel drafted the original manuscript. Abu Reza Md. Towfiqul Islam and Ahmed Awad reviewed the manuscript. All authors read and approved the final version of manuscript.

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Correspondence to Navsal Kumar.

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Elbeltagi, A., Kumar, N., Chandel, A. et al. Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data. Environ Monit Assess 194, 141 (2022). https://doi.org/10.1007/s10661-022-09812-0

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