Deep learning for daily potential evapotranspiration using a HS-LSTM approach

Yan, Xiaohui; Yang, Na; Ao, Ruigui; Mohammadian, Abdolmajid; Liu, Jianwei; Cao, Huade; Yin, Penghai

doi:10.1016/j.atmosres.2023.106856

Atmospheric Research

2023

DOI: 10.1016/j.atmosres.2023.106856

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Deep learning for daily potential evapotranspiration using a HS-LSTM approach

Xiaohui Yan

Na Yang

Ruigui Ao

et al.

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Cited by 6 publications

References 60 publications

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Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration

Amani,

Shafizadeh-Moghadam,

Morid

2024

Water Resour Manage

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The current study evaluated the accuracy of four machine learning (ML) techniques and thirteen experimental methods calibrated to estimate potential evapotranspiration (ET0) in arid and semi-arid regions. Various scenarios utilizing meteorological data were examined, and FAO56-PM was used as a benchmark. The results revealed that the ML models outperformed the experimental methods at both daily and monthly scales. Among the ML models, the artificial neural networks (ANNs), generalized additive model (GAM), random forest (RF), and support vector machine (SVM), respectively, demonstrated higher accuracy on a monthly scale, while the ANNs, SVM, RF, and GAM exhibited greater accuracy on a daily scale.Notably, the ANNs and SVM achieved high accuracy even with a limited number of variables.Conversely, the accuracy of the RF improved with an increased number of variables.Comparing ML models to experimental models with equivalent input revealed that ANN with inputs similar to Valiantras-1 performed better on a monthly scale, while SVM with inputs akin to Valiantras-3 showed superior performance on a daily scale. Our findings suggest that average temperature, wind speed, and sunshine hours contribute significantly to the accuracy of ML models. Consequently, these ML models can serve as an alternative to the FAO56-PM method for estimating ET0.

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Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration

Amani,

Shafizadeh-Moghadam,

Morid

2024

Water Resour Manage

View full text Add to dashboard Cite

show abstract

Accuracy and interpretability of machine learning-based approaches for daily ETo estimation under semi-arid climate in the West African Sahel

Yonaba,

Kiema,

Tazen

et al. 2024

Earth Sci Inform

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Development of an enhanced bidirectional recurrent neural network combined with time-varying filter-based empirical mode decomposition to forecast weekly reference evapotranspiration

Karbasi,

Jamei,

Ali

et al. 2023

Agricultural Water Management

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Deep learning for daily potential evapotranspiration using a HS-LSTM approach

Cited by 6 publications

References 60 publications

Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration

Utilizing Machine Learning Models with Limited Meteorological Data as Alternatives for the FAO-56PM Model in Estimating Reference Evapotranspiration

Accuracy and interpretability of machine learning-based approaches for daily ETo estimation under semi-arid climate in the West African Sahel

Development of an enhanced bidirectional recurrent neural network combined with time-varying filter-based empirical mode decomposition to forecast weekly reference evapotranspiration

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