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Deep learning approaches for short-crop reference evapotranspiration estimation: a case study in Southeastern Australia

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Abstract

Evapotranspiration (ETo) plays a crucial role in managing water resources and agricultural water consumption. It is also commonly used to quantify the total amount of water lost through a number of important processes that occur among the land and atmosphere. In this research, four deep learning algorithms—CNN, DNN, BiLSTM, and GRU—were applied to predict evapotranspiration based on 14 years of daily data from Victoria, a state in southeastern Australia. The data sample was split into two periods: nine years (2010–2019) for training and four years (2020–2023) for testing. Deep learning algorithms have good performance for predicting evapotranspiration. The results showed that the GRU and DNN models were slightly better than the other two models. In the testing phases, the GRU models found R-Square, RSME, MSE, and MAE values, 0.989, 0.1794, 0.0322, and 0.1417, respectively, while the DNN models performed 0.980, 0.185, 0.0345, and 0.1507 value of R-Square, RMSE, MSE, and MAE, respectively, which indicated the GRU model perform better than other models. The CNN model achieved an R² of 0.958, with an RMSE of 0.364 and an MSE of 0.1330, indicating less precise estimations. Similarly, the BiLSTM model performed better than CNN but still lagged behind GRU and DNN, with an R² of 0.969 and an MSE of 0.0988. Moreover, deep learning models perform well, the GRU model has comparatively excellent performance than other DL models. It has been suggested that the most accurate model to improve future studies on evapotranspiration estimations is the GRU model, which could improve irrigation efficiency and boost crop productivity.  

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Data availability

Data will be make available on request.

Abbreviations

ETo:

Reference Evapotranspiration

CNN:

Convolutional Neural Network

DNN:

Deep Neural Network

BiLSTM:

Bidirectional Long Short-Term Memory

GRU:

Gated Recurrent Unit

R-Square (R²):

Coefficient of Determination

RMSE:

Root Mean Square Error

MSE:

Mean Squared Error

MAE:

Mean Absolute Error

FAO:

Food and Agriculture Organization

MJ/m²:

Megajoules per square meter

SD:

Standard Deviation

ANN:

Artificial Neural Network

ELM:

Extreme Learning Machine

XGBoost:

Extreme Gradient Boosting

SVM:

Support Vector Machine

MARS:

Multivariate Adaptive Regression Splines

RF:

Random Forest

LightGBM:

Light Gradient Boosting Machine

MLP:

Multi-Layer Perceptron

EMD:

Empirical Mode Decomposition

MAPE:

Mean Absolute Percentage Error

R:

Correlation Coefficient

SD:

Standard Deviation

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Funding

This research did not receive any specific grant from the funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet, 3114, Bangladesh

    Uaktho Baishnab

  2. Department of Computer Science and Engineering, Varendra University, Rajshahi, Bangladesh

    Md. Sahadat Hossen Sajib

  3. Department of computer science and engineering, Daffodil international university, Dhaka, Bangladesh

    Ashraful Islam

  4. Department of Geography & Environment, Jagannath University, Dhaka, Bangladesh

    Shangida Akter

  5. Department of Economics, University of Rajshahi, Rajshahi, Bangladesh

    Atik Hasan

  6. Department of Data Analytics & Information Systems, Utah State University, Logan, UT, USA

    Tonmoy Roy

  7. Department of Civil Engineering, Leading University, Sylhet, Bangladesh

    Pobithra Das

Authors
  1. Uaktho Baishnab
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  2. Md. Sahadat Hossen Sajib
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  3. Ashraful Islam
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  4. Shangida Akter
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  5. Atik Hasan
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  6. Tonmoy Roy
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  7. Pobithra Das
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Contributions

U.B: Conceptualization, Writing— original draft, Data analysis, Methodology, Software, Formal analysis, Writing – review & editing. Md. S.H.S: Conceptualization, Writing— original draft, Data analysis, Software, Methodology, Writing-review and editing. A.I: Writing— original draft, Formal analysis, Data analysis, Software, and Methodology. S.A: Methodology, Data curation, Formal analysis, Writing-review and editing. A.H: Writing— original draft, Data curation, Formal analysis. P.D: Supervision, Writing— original draft, Data curation, Formal analysis.

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Correspondence to Pobithra Das.

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Communicated by Hassan Babaie.

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Baishnab, U., Hossen Sajib, M.S., Islam, A. et al. Deep learning approaches for short-crop reference evapotranspiration estimation: a case study in Southeastern Australia. Earth Sci Inform 18, 4 (2025). https://doi.org/10.1007/s12145-024-01616-9

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  • DOI: https://doi.org/10.1007/s12145-024-01616-9

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