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Automated actual evapotranspiration estimation : Hybrid model of a novel attention based U-Net and metaheuristic optimization algorithms

Ghaderi Bafti, Alireza ; Ahmadi, Arman ; Abbasi, Ali ; Kamangir, Hamid ; Jamali, Sadegh LU orcid and Hashemi, Hossein LU orcid (2024) In Atmospheric Research
Abstract
Actual evapotranspiration (ETa) plays a crucial role in the water and energy cycles of the earth. An accurate estimate of the ETa is essential for management of the water resources, agriculture, and irrigation, as well as research on atmospheric variations. Despite the importance of accurate ETa values, estimating and mapping them remains challenging due to the physical and biological complexity of the ET process. As a novel approach for rapid and reliable estimation of ETa, the present study develops automated deep learning (AutoDL) models that incorporate a metaheuristic optimization algorithm for image processing, architectural design, and hyperparameter tuning. The proposed AutoDL models integrate three different spatial and channel... (More)
Actual evapotranspiration (ETa) plays a crucial role in the water and energy cycles of the earth. An accurate estimate of the ETa is essential for management of the water resources, agriculture, and irrigation, as well as research on atmospheric variations. Despite the importance of accurate ETa values, estimating and mapping them remains challenging due to the physical and biological complexity of the ET process. As a novel approach for rapid and reliable estimation of ETa, the present study develops automated deep learning (AutoDL) models that incorporate a metaheuristic optimization algorithm for image processing, architectural design, and hyperparameter tuning. The proposed AutoDL models integrate three different spatial and channel attention mechanisms, including a novel activated spatial attention mechanism (ASPAM), with the U-Net architecture. Bypassing the need for meteorological inputs, the proposed framework uses Moderate Resolution Imaging Spectrometer (MODIS) products and Digital Elevation Model (DEM) data as inputs. To evaluate the performance of the models, they are applied to three study areas in the United States with various climatic characteristics. According to the results, during the spring and summer, when the target values have higher certainty, the estimations are highly promising, with R2 as high as 0.91 and MAPE as low as 6.40%. Furthermore, the proposed ASPAM module improves the accuracy of ETa estimations compared to attention gate (AG) and squeeze and excitation (SE) attention modules. The results also indicate that the MODIS raw products and derived vegetation and water indices can predict ETa within a reliable range of accuracy, with the addition of DEM data marginally enhancing the models' performance. The automatic workflow of this model makes it significantly easy to use, contributing to its applicability and generalizability for enhancing atmospheric research. (Less)
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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Atmospheric Research
pages
16 pages
publisher
Elsevier
external identifiers
  • scopus:85177058090
ISSN
0169-8095
DOI
10.1016/j.atmosres.2023.107107
language
English
LU publication?
yes
id
8cb10345-fbfb-4770-90ba-e9ec93debe77
date added to LUP
2023-11-27 13:14:30
date last changed
2023-12-21 10:13:09
@article{8cb10345-fbfb-4770-90ba-e9ec93debe77,
  abstract     = {{Actual evapotranspiration (ETa) plays a crucial role in the water and energy cycles of the earth. An accurate estimate of the ETa is essential for management of the water resources, agriculture, and irrigation, as well as research on atmospheric variations. Despite the importance of accurate ETa values, estimating and mapping them remains challenging due to the physical and biological complexity of the ET process. As a novel approach for rapid and reliable estimation of ETa, the present study develops automated deep learning (AutoDL) models that incorporate a metaheuristic optimization algorithm for image processing, architectural design, and hyperparameter tuning. The proposed AutoDL models integrate three different spatial and channel attention mechanisms, including a novel activated spatial attention mechanism (ASPAM), with the U-Net architecture. Bypassing the need for meteorological inputs, the proposed framework uses Moderate Resolution Imaging Spectrometer (MODIS) products and Digital Elevation Model (DEM) data as inputs. To evaluate the performance of the models, they are applied to three study areas in the United States with various climatic characteristics. According to the results, during the spring and summer, when the target values have higher certainty, the estimations are highly promising, with R2 as high as 0.91 and MAPE as low as 6.40%. Furthermore, the proposed ASPAM module improves the accuracy of ETa estimations compared to attention gate (AG) and squeeze and excitation (SE) attention modules. The results also indicate that the MODIS raw products and derived vegetation and water indices can predict ETa within a reliable range of accuracy, with the addition of DEM data marginally enhancing the models' performance. The automatic workflow of this model makes it significantly easy to use, contributing to its applicability and generalizability for enhancing atmospheric research.}},
  author       = {{Ghaderi Bafti, Alireza and Ahmadi, Arman and Abbasi, Ali and Kamangir, Hamid and Jamali, Sadegh and Hashemi, Hossein}},
  issn         = {{0169-8095}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Atmospheric Research}},
  title        = {{Automated actual evapotranspiration estimation : Hybrid model of a novel attention based U-Net and metaheuristic optimization algorithms}},
  url          = {{http://dx.doi.org/10.1016/j.atmosres.2023.107107}},
  doi          = {{10.1016/j.atmosres.2023.107107}},
  year         = {{2024}},
}