Bayesian-optimized machine learning boosts actual evapotranspiration prediction in water-stressed agricultural regions of China

The accurate estimation of actual evapotranspiration (AET) is crucial for sustainable water resource management, especially in water-scarce and agriculturally intensive regions like Beijing and Tianjin, China. Traditional methods for AET estimation, whether empirical or physically based, often face...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Scientific reports Ročník 15; číslo 1; s. 37548 - 18
Hlavní autoři: Elbeltagi, Ahmed, Srivastava, Aman, Cao, Xinchun, Gautam, Vinay Kumar, Zerouali, Bilel, Aslam, Muhammad Rizwan, Salem, Ali, Emami, Hojjat, Elsadek, Elsayed Ahmed
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 28.10.2025
Nature Publishing Group
Nature Portfolio
Témata:
704/106/242
704/106/694
704/172/4081
704/242
Agricultural hydrology
Agricultural water management
Artificial intelligence
Bayesian analysis
Bayesian optimization
Climate change
Climate data modeling
Data-driven evapotranspiration
Datasets
Drought
Environmental monitoring
Evapotranspiration
Humanities and Social Sciences
Irrigation scheduling
Learning algorithms
Machine learning
Mathematical models
multidisciplinary
Neural networks
Resource management
Science
Science (multidisciplinary)
Solar radiation
Vapor pressure
Water management
Water resources management
Wind speed
Beijing China
China
Data-driven evapotranspiration
Agricultural hydrology
Irrigation scheduling
Bayesian optimization
Climate data modeling
Agricultural water management
ISSN:2045-2322, 2045-2322
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract The accurate estimation of actual evapotranspiration (AET) is crucial for sustainable water resource management, especially in water-scarce and agriculturally intensive regions like Beijing and Tianjin, China. Traditional methods for AET estimation, whether empirical or physically based, often face limitations due to high data requirements, limited scalability, and sensitivity to input uncertainties. This creates a critical research gap in providing reliable AET predictions under data-limited conditions. To address this, we evaluated the efficacy of integrating four advanced machine learning (ML) models: Support Vector Machine (SVM), Gaussian Process Regression (GPR), Ensemble Tree, and Neural Network, with Bayesian hyperparameter optimization for AET modeling using the high-resolution TerraClimate dataset spanning 1958–2022. Key meteorological variables, including maximum and minimum temperature (T max and T min ), solar radiation (SR), wind speed (WS), vapor pressure deficit (VPD), and precipitation (PPT), were selected through rigorous correlation and multicollinearity analyses. Model performance was assessed using the coefficient of determination (R 2 ), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) on a 75:25 train-test split. Results demonstrate that the optimizable GPR model achieved the highest predictive accuracy (RMSE = 5.54, R 2  = 0.98 on test data), outperforming other ML approaches and traditional empirical models. PPT, T min , and T max emerged as the most influential predictors for AET. Our findings reveal that ML models, particularly when optimized via Bayesian techniques, yield a robust, scalable, and data-efficient alternative for AET estimation in regions with limited meteorological records. This study establishes a new benchmark for AET modeling, with significant implications for irrigation scheduling, drought monitoring, and integrated water management in the North China Plain and comparable agro-ecological regions.
AbstractList The accurate estimation of actual evapotranspiration (AET) is crucial for sustainable water resource management, especially in water-scarce and agriculturally intensive regions like Beijing and Tianjin, China. Traditional methods for AET estimation, whether empirical or physically based, often face limitations due to high data requirements, limited scalability, and sensitivity to input uncertainties. This creates a critical research gap in providing reliable AET predictions under data-limited conditions. To address this, we evaluated the efficacy of integrating four advanced machine learning (ML) models: Support Vector Machine (SVM), Gaussian Process Regression (GPR), Ensemble Tree, and Neural Network, with Bayesian hyperparameter optimization for AET modeling using the high-resolution TerraClimate dataset spanning 1958–2022. Key meteorological variables, including maximum and minimum temperature (Tmax and Tmin), solar radiation (SR), wind speed (WS), vapor pressure deficit (VPD), and precipitation (PPT), were selected through rigorous correlation and multicollinearity analyses. Model performance was assessed using the coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) on a 75:25 train-test split. Results demonstrate that the optimizable GPR model achieved the highest predictive accuracy (RMSE = 5.54, R2 = 0.98 on test data), outperforming other ML approaches and traditional empirical models. PPT, Tmin, and Tmax emerged as the most influential predictors for AET. Our findings reveal that ML models, particularly when optimized via Bayesian techniques, yield a robust, scalable, and data-efficient alternative for AET estimation in regions with limited meteorological records. This study establishes a new benchmark for AET modeling, with significant implications for irrigation scheduling, drought monitoring, and integrated water management in the North China Plain and comparable agro-ecological regions.
The accurate estimation of actual evapotranspiration (AET) is crucial for sustainable water resource management, especially in water-scarce and agriculturally intensive regions like Beijing and Tianjin, China. Traditional methods for AET estimation, whether empirical or physically based, often face limitations due to high data requirements, limited scalability, and sensitivity to input uncertainties. This creates a critical research gap in providing reliable AET predictions under data-limited conditions. To address this, we evaluated the efficacy of integrating four advanced machine learning (ML) models: Support Vector Machine (SVM), Gaussian Process Regression (GPR), Ensemble Tree, and Neural Network, with Bayesian hyperparameter optimization for AET modeling using the high-resolution TerraClimate dataset spanning 1958–2022. Key meteorological variables, including maximum and minimum temperature (T max and T min ), solar radiation (SR), wind speed (WS), vapor pressure deficit (VPD), and precipitation (PPT), were selected through rigorous correlation and multicollinearity analyses. Model performance was assessed using the coefficient of determination (R 2 ), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) on a 75:25 train-test split. Results demonstrate that the optimizable GPR model achieved the highest predictive accuracy (RMSE = 5.54, R 2  = 0.98 on test data), outperforming other ML approaches and traditional empirical models. PPT, T min , and T max emerged as the most influential predictors for AET. Our findings reveal that ML models, particularly when optimized via Bayesian techniques, yield a robust, scalable, and data-efficient alternative for AET estimation in regions with limited meteorological records. This study establishes a new benchmark for AET modeling, with significant implications for irrigation scheduling, drought monitoring, and integrated water management in the North China Plain and comparable agro-ecological regions.
Abstract The accurate estimation of actual evapotranspiration (AET) is crucial for sustainable water resource management, especially in water-scarce and agriculturally intensive regions like Beijing and Tianjin, China. Traditional methods for AET estimation, whether empirical or physically based, often face limitations due to high data requirements, limited scalability, and sensitivity to input uncertainties. This creates a critical research gap in providing reliable AET predictions under data-limited conditions. To address this, we evaluated the efficacy of integrating four advanced machine learning (ML) models: Support Vector Machine (SVM), Gaussian Process Regression (GPR), Ensemble Tree, and Neural Network, with Bayesian hyperparameter optimization for AET modeling using the high-resolution TerraClimate dataset spanning 1958–2022. Key meteorological variables, including maximum and minimum temperature (Tmax and Tmin), solar radiation (SR), wind speed (WS), vapor pressure deficit (VPD), and precipitation (PPT), were selected through rigorous correlation and multicollinearity analyses. Model performance was assessed using the coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) on a 75:25 train-test split. Results demonstrate that the optimizable GPR model achieved the highest predictive accuracy (RMSE = 5.54, R2 = 0.98 on test data), outperforming other ML approaches and traditional empirical models. PPT, Tmin, and Tmax emerged as the most influential predictors for AET. Our findings reveal that ML models, particularly when optimized via Bayesian techniques, yield a robust, scalable, and data-efficient alternative for AET estimation in regions with limited meteorological records. This study establishes a new benchmark for AET modeling, with significant implications for irrigation scheduling, drought monitoring, and integrated water management in the North China Plain and comparable agro-ecological regions.
ArticleNumber 37548
Author Zerouali, Bilel
Elsadek, Elsayed Ahmed
Cao, Xinchun
Aslam, Muhammad Rizwan
Elbeltagi, Ahmed
Salem, Ali
Emami, Hojjat
Gautam, Vinay Kumar
Srivastava, Aman
Author_xml – sequence: 1
  givenname: Ahmed
  surname: Elbeltagi
  fullname: Elbeltagi, Ahmed
  organization: College of Agricultural Science and Engineering, Hohai University, Agricultural Engineering Dept., Faculty of Agriculture, Mansoura University
– sequence: 2
  givenname: Aman
  surname: Srivastava
  fullname: Srivastava, Aman
  organization: Formerly, Centre for Technology Alternatives for Rural Areas (CTARA), Indian Institute of Technology (IIT) Bombay
– sequence: 3
  givenname: Xinchun
  surname: Cao
  fullname: Cao, Xinchun
  email: caoxinchun@hhu.edu.cn
  organization: College of Agricultural Science and Engineering, Hohai University, Department of Bioresource Engineering, Faculty of Agricultural & Environmental Sciences, McGill University
– sequence: 4
  givenname: Vinay Kumar
  surname: Gautam
  fullname: Gautam, Vinay Kumar
  organization: School of Natural Resource Management, College of Post Graduate Studies in Agricultural Sciences, Central Agricultural University, (Imphal)
– sequence: 5
  givenname: Bilel
  surname: Zerouali
  fullname: Zerouali, Bilel
  organization: Laboratory of Architecture, Cities and Environment, Department of Hydraulic, Faculty of Civil Engineering and Architecture, University of Chlef, Hassiba Benbouali
– sequence: 6
  givenname: Muhammad Rizwan
  surname: Aslam
  fullname: Aslam, Muhammad Rizwan
  organization: College of Environmental and Resource Sciences, Zhejiang University
– sequence: 7
  givenname: Ali
  surname: Salem
  fullname: Salem, Ali
  email: salem.ali@mik.pte.hu
  organization: Civil Engineering Department, Faculty of Engineering, Minia University, Structural Diagnostics and Analysis Research Group, Faculty of Engineering and Information Technology, University of Pécs
– sequence: 8
  givenname: Hojjat
  surname: Emami
  fullname: Emami, Hojjat
  organization: Department of Computer Engineering, Bonab University
– sequence: 9
  givenname: Elsayed Ahmed
  surname: Elsadek
  fullname: Elsadek, Elsayed Ahmed
  organization: Biosystems Engineering Department, University of Arizona, Agricultural and Biosystems Engineering Department, College of Agriculture, Damietta University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/41152585$$D View this record in MEDLINE/PubMed
BookMark eNp9kktv1TAQhS1UREvpH2CBIrFhE_AzyV3CFY9KldjA2ho7k-CrxA62Q3XZ8NcxN6UgFnjjsfWdM2P5PCZnPngk5CmjLxkV3askmdp1NeWq5pwJWh8fkAtOZTkKzs_-qs_JVUoHWpbiO8l2j8i5ZExx1akL8uMNHDE58HVYspvdd-yrGewX57GaEKJ3fqxMCCmnCmxeYarwGywhR_BpcRGyC75aIvbOnkrnq1vIGOuUI6ZU7GCMzq5TXmMRRxwLlaowVPvSBJ6QhwNMCa_u9kvy-d3bT_sP9c3H99f71ze1laLLdSfkwMBQ2-5axppGUiiVYNSagRujsG0tAG8EUGhaA9h31DBOQaLqgXFxSa433z7AQS_RzRCPOoDTp4sQRw0xOzuhNsow0bIGQVrZcIS2UYxiZwDkINlQvF5sXksMX1dMWc8uWZwm8BjWpAUvA8qOKlXQ5_-gh7BGX156olgn2x0r1LM7ajUz9vfj_f6mAvANsDGkFHG4RxjVv-KgtzjoEgd9ioM-FpHYRKnAfsT4p_d_VD8BNbK5ew
Cites_doi 10.3390/su14052601
10.1007/s41060-025-00762-7
10.1007/s13201-023-01941-2
10.1016/j.jhydrol.2023.129361
10.1016/j.agrformet.2023.109513
10.1016/j.eiar.2022.106861
10.1016/j.ecolind.2017.12.047
10.1016/j.heliyon.2023.e15355
10.1016/j.agwat.2024.108673
10.1016/j.heliyon.2023.e18819
10.1007/s13369-012-0214-5
10.3390/rs12020332
10.1002/ird.2707
10.1175/JHM-D-19-0298.1
10.17694/bajece.679662
10.1016/j.agrformet.2023.109579
10.1016/j.agwat.2022.108038
10.1002/hyp.7439
10.1007/s00271-020-00680-5
10.1016/j.agwat.2020.106210
10.3390/w15061149
10.1016/j.compag.2022.106687
10.3390/agronomy13020609
10.1016/j.wen.2021.10.002
10.1016/j.fcr.2017.10.009
10.1007/978-3-030-89010-0_10
10.1007/978-3-642-20617-7_6563
10.1007/s13201-024-02308-x
10.1016/j.atmosres.2023.107107
10.1007/s13201-023-01942-1
10.1016/j.catena.2025.109314
10.1007/s40031-021-00684-5
10.1016/j.agwat.2020.106454
10.1016/j.jhydrol.2016.10.022
10.1007/s11356-022-21410-8
10.3390/rs13122393
10.1007/s00704-025-05406-1
10.1007/s11704-019-8208-z
10.3390/w15030450
10.1016/j.rineng.2024.103135
10.1038/s41598-025-05985-z
10.3390/hydrology9070123
10.2166/nh.2023.040
10.1080/24749508.2024.2429842
10.1016/j.agwat.2023.108499
10.1007/s11269-022-03067-7
10.1109/TCYB.2022.3164542
10.3390/su14105771
10.1016/j.ejrh.2025.102339
10.1016/j.geoderma.2025.117440
10.1080/19443994.2012.748443
10.1007/s00704-024-04862-5
10.1007/s11442-013-1015-9
10.1145/3018896.3065830
10.1007/s11269-023-03674-y
10.1016/j.ecolind.2023.110837
10.1016/j.agwat.2021.107343
10.1080/19475705.2025.2468850
10.1016/j.ejrh.2024.102043
10.3390/ijerph192013127
10.1016/j.jhydrol.2020.125286
10.1016/j.jhydrol.2019.03.028
10.1016/j.jhydrol.2016.02.053
10.2166/wst.2023.137
10.1080/02626667.2022.2083511
10.1007/s00271-025-01019-8
10.1016/j.ecolind.2025.114012
10.3390/info15040235
10.1016/j.envc.2022.100483
10.1007/s00521-023-08466-4
10.3390/earth4030038
10.1029/2021WR031069
10.1007/s10845-024-02405-3
ContentType Journal Article
Copyright The Author(s) 2025
The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: The Author(s) 2025
– notice: The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID C6C
AAYXX
CITATION
NPM
3V.
7X7
7XB
88A
88E
88I
8FE
8FH
8FI
8FJ
8FK
ABUWG
AEUYN
AFKRA
AZQEC
BBNVY
BENPR
BHPHI
CCPQU
DWQXO
FYUFA
GHDGH
GNUQQ
HCIFZ
K9.
LK8
M0S
M1P
M2P
M7P
PHGZM
PHGZT
PIMPY
PJZUB
PKEHL
PPXIY
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
Q9U
7X8
DOA
DOI 10.1038/s41598-025-22130-y
DatabaseName Springer Nature Link
CrossRef
PubMed
ProQuest Central (Corporate)
Health & Medical Collection
ProQuest Central (purchase pre-March 2016)
Biology Database (Alumni Edition)
Medical Database (Alumni Edition)
Science Database (Alumni Edition)
ProQuest SciTech Collection
ProQuest Natural Science Collection
Hospital Premium Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
ProQuest Central (Alumni)
ProQuest One Sustainability
ProQuest Central UK/Ireland
ProQuest Central Essentials - QC
Biological Science Collection
ProQuest Central
Natural Science Collection
ProQuest One
ProQuest Central Korea
Proquest Health Research Premium Collection
Health Research Premium Collection (Alumni)
ProQuest Central Student
SciTech Premium Collection
ProQuest Health & Medical Complete (Alumni)
Biological Sciences
ProQuest Health & Medical Collection
Medical Database
Science Database
Biological Science Database (ProQuest)
ProQuest Central Premium
ProQuest One Academic
Proquest Open Access Content
ProQuest Health & Medical Research Collection
ProQuest One Academic Middle East (New)
ProQuest One Health & Nursing
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
ProQuest Central Basic
MEDLINE - Academic
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
PubMed
Publicly Available Content Database
ProQuest Central Student
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest Health & Medical Complete (Alumni)
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest One Health & Nursing
ProQuest Natural Science Collection
ProQuest Central China
ProQuest Biology Journals (Alumni Edition)
ProQuest Central
ProQuest One Applied & Life Sciences
ProQuest One Sustainability
ProQuest Health & Medical Research Collection
Health Research Premium Collection
Health and Medicine Complete (Alumni Edition)
Natural Science Collection
ProQuest Central Korea
Health & Medical Research Collection
Biological Science Collection
ProQuest Central (New)
ProQuest Medical Library (Alumni)
ProQuest Science Journals (Alumni Edition)
ProQuest Biological Science Collection
ProQuest Central Basic
ProQuest Science Journals
ProQuest One Academic Eastern Edition
ProQuest Hospital Collection
Health Research Premium Collection (Alumni)
Biological Science Database
ProQuest SciTech Collection
ProQuest Hospital Collection (Alumni)
ProQuest Health & Medical Complete
ProQuest Medical Library
ProQuest One Academic UKI Edition
ProQuest One Academic
ProQuest One Academic (New)
ProQuest Central (Alumni)
MEDLINE - Academic
DatabaseTitleList Publicly Available Content Database
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 3
  dbid: PIMPY
  name: ProQuest - Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Biology
EISSN 2045-2322
EndPage 18
ExternalDocumentID oai_doaj_org_article_b5b13716ea4c462ea76510e8baa4f41f
41152585
10_1038_s41598_025_22130_y
Genre Journal Article
GeographicLocations Beijing China
China
GeographicLocations_xml – name: China
– name: Beijing China
GroupedDBID 0R~
4.4
53G
5VS
7X7
88E
88I
8FE
8FH
8FI
8FJ
AAFWJ
AAJSJ
AAKDD
AASML
ABDBF
ABUWG
ACGFS
ACUHS
ADBBV
ADRAZ
AENEX
AEUYN
AFFHD
AFKRA
AFPKN
ALMA_UNASSIGNED_HOLDINGS
AOIJS
AZQEC
BAWUL
BBNVY
BCNDV
BENPR
BHPHI
BPHCQ
BVXVI
C6C
CCPQU
DIK
DWQXO
EBD
EBLON
EBS
ESX
FYUFA
GNUQQ
GROUPED_DOAJ
GX1
HCIFZ
HH5
HMCUK
HYE
KQ8
LK8
M1P
M2P
M7P
M~E
NAO
OK1
PHGZM
PHGZT
PIMPY
PJZUB
PPXIY
PQGLB
PQQKQ
PROAC
PSQYO
RNT
RNTTT
RPM
SNYQT
UKHRP
AAYXX
CITATION
NPM
3V.
7XB
88A
8FK
K9.
M48
PKEHL
PQEST
PQUKI
PRINS
Q9U
7X8
ID FETCH-LOGICAL-c438t-834f1ab0c797116640a797310cbf2bb5e77caa263a0a67baed80b120a4e5da123
IEDL.DBID BENPR
ISICitedReferencesCount 0
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001604676500028&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2045-2322
IngestDate Mon Nov 10 19:21:16 EST 2025
Wed Oct 29 17:09:51 EDT 2025
Wed Oct 29 07:54:49 EDT 2025
Mon Nov 03 01:48:52 EST 2025
Wed Nov 05 20:58:46 EST 2025
Wed Oct 29 01:23:32 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Keywords Data-driven evapotranspiration
Agricultural hydrology
Irrigation scheduling
Bayesian optimization
Climate data modeling
Agricultural water management
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c438t-834f1ab0c797116640a797310cbf2bb5e77caa263a0a67baed80b120a4e5da123
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
OpenAccessLink https://www.proquest.com/docview/3266184791?pq-origsite=%requestingapplication%
PMID 41152585
PQID 3266184791
PQPubID 2041939
PageCount 18
ParticipantIDs doaj_primary_oai_doaj_org_article_b5b13716ea4c462ea76510e8baa4f41f
proquest_miscellaneous_3266448055
proquest_journals_3266184791
pubmed_primary_41152585
crossref_primary_10_1038_s41598_025_22130_y
springer_journals_10_1038_s41598_025_22130_y
PublicationCentury 2000
PublicationDate 2025-10-28
PublicationDateYYYYMMDD 2025-10-28
PublicationDate_xml – month: 10
  year: 2025
  text: 2025-10-28
  day: 28
PublicationDecade 2020
PublicationPlace London
PublicationPlace_xml – name: London
– name: England
PublicationTitle Scientific reports
PublicationTitleAbbrev Sci Rep
PublicationTitleAlternate Sci Rep
PublicationYear 2025
Publisher Nature Publishing Group UK
Nature Publishing Group
Nature Portfolio
Publisher_xml – name: Nature Publishing Group UK
– name: Nature Publishing Group
– name: Nature Portfolio
References J Sui (22130_CR5) 2018; 215
J Luo (22130_CR66) 2022
E Elsadek (22130_CR1) 2023; 13
A Elbeltagi (22130_CR28) 2023; 35
GA Araujo-Carrillo (22130_CR39) 2025; 156
Y Agrawal (22130_CR31) 2022; 36
AA Abro (22130_CR47) 2020; 8
S Li (22130_CR54) 2016; 543
AG Bafti (22130_CR71) 2024; 297
EA Elsadek (22130_CR6) 2025
X Dong (22130_CR48) 2020; 14
SS Eslamian (22130_CR53) 2012; 37
X Shao (22130_CR22) 2022; 58
S Wanniarachchi (22130_CR3) 2022; 9
K Xue (22130_CR14) 2023; 337
DM D’Addona (22130_CR51) 2014
22130_CR57
P Rai (22130_CR30) 2022; 14
PS Tulla (22130_CR78) 2024; 155
MY Chia (22130_CR33) 2022; 261
N Paciolla (22130_CR16) 2023; 339
E-SM El-kenawy (22130_CR27) 2022; 29
P Paredes (22130_CR18) 2020; 240
S Wang (22130_CR36) 2018; 87
A Elbeltagi (22130_CR42) 2024; 56
T Suwanlertcharoen (22130_CR17) 2023; 15
S Hu (22130_CR37) 2022; 97
T Wang (22130_CR61) 2023; 53
22130_CR50
L Zhao (22130_CR4) 2013; 23
Z Abda (22130_CR29) 2022; 67
K Sharma (22130_CR9) 2021; 243
R Li (22130_CR75) 2025; 259
W Cai (22130_CR24) 2021; 13
M Sabino (22130_CR58) 2023; 4
L Yin (22130_CR64) 2023; 154
EA Elsadek (22130_CR7) 2024; 292
22130_CR49
SL Dingman (22130_CR52) 2015
M Jamei (22130_CR35) 2022; 193
A Elbeltagi (22130_CR68) 2023; 15
E Gomaa (22130_CR25) 2023; 9
A Rani (22130_CR60) 2022; 7
J Li (22130_CR63) 2020; 21
M Al-Mukhtar (22130_CR77) 2024; 38
A Elbeltagi (22130_CR2) 2024; 14
LW Rizkallah (22130_CR43) 2025
C Niu (22130_CR32) 2023; 54
EA Elsadek (22130_CR8) 2023
H Rahimikhoob (22130_CR13) 2020; 38
R Kins (22130_CR45) 2025; 36
22130_CR40
K Gudulas (22130_CR56) 2013; 51
E Zoratipour (22130_CR21) 2023; 13
AG Hamedani (22130_CR69) 2021; 4
M Kadkhodazadeh (22130_CR10) 2022; 14
Y Yang (22130_CR38) 2025; 178
Y Zhang (22130_CR55) 2023; 289
S Lu (22130_CR74) 2025; 460
H-J Kim (22130_CR20) 2023; 275
B Cemek (22130_CR23) 2023; 13
GI Ezenne (22130_CR15) 2023; 25
A Husnain (22130_CR46) 2024; 24
LB Ferreira (22130_CR65) 2019; 572
H Darabi (22130_CR19) 2023; 54
J Rajput (22130_CR70) 2023; 87
R Guido (22130_CR44) 2024; 15
Y Feng (22130_CR67) 2016; 536
B Zerouali (22130_CR26) 2023; 9
B Wu (22130_CR62) 2023; 619
Z Chen (22130_CR72) 2020; 591
22130_CR76
AF Tarı (22130_CR11) 2022; 71
A Ahmadi (22130_CR73) 2025; 59
AS Sumant (22130_CR34) 2022; 103
B Wu (22130_CR12) 2020; 12
K Oukaddour (22130_CR41) 2025; 16
M Achite (22130_CR79) 2025; 15
J Estévez (22130_CR59) 2009; 23
References_xml – volume: 14
  start-page: 2601
  year: 2022
  ident: 22130_CR10
  publication-title: Sustainability
  doi: 10.3390/su14052601
– year: 2025
  ident: 22130_CR43
  publication-title: Int. J. Data Sci. Anal.
  doi: 10.1007/s41060-025-00762-7
– volume: 13
  start-page: 137
  year: 2023
  ident: 22130_CR21
  publication-title: Appl. Water Sci.
  doi: 10.1007/s13201-023-01941-2
– volume: 619
  start-page: 129361
  year: 2023
  ident: 22130_CR62
  publication-title: J. Hydrol.
  doi: 10.1016/j.jhydrol.2023.129361
– volume: 337
  start-page: 109513
  year: 2023
  ident: 22130_CR14
  publication-title: Agric. For. Meteorol.
  doi: 10.1016/j.agrformet.2023.109513
– volume: 97
  start-page: 106861
  year: 2022
  ident: 22130_CR37
  publication-title: Environ. Impact Assess. Rev.
  doi: 10.1016/j.eiar.2022.106861
– volume: 87
  start-page: 107
  year: 2018
  ident: 22130_CR36
  publication-title: Ecol. Indic.
  doi: 10.1016/j.ecolind.2017.12.047
– volume: 9
  start-page: e15355
  year: 2023
  ident: 22130_CR26
  publication-title: Heliyon
  doi: 10.1016/j.heliyon.2023.e15355
– volume: 292
  year: 2024
  ident: 22130_CR7
  publication-title: Agric. Water Manag.
  doi: 10.1016/j.agwat.2024.108673
– volume: 9
  start-page: e18819
  year: 2023
  ident: 22130_CR25
  publication-title: Heliyon
  doi: 10.1016/j.heliyon.2023.e18819
– volume: 37
  start-page: 935
  year: 2012
  ident: 22130_CR53
  publication-title: Arab. J. Sci. Eng.
  doi: 10.1007/s13369-012-0214-5
– volume: 12
  start-page: 332
  year: 2020
  ident: 22130_CR12
  publication-title: Remote Sens.
  doi: 10.3390/rs12020332
– volume: 71
  start-page: 948
  year: 2022
  ident: 22130_CR11
  publication-title: Irrig. Drain.
  doi: 10.1002/ird.2707
– volume: 21
  start-page: 891
  year: 2020
  ident: 22130_CR63
  publication-title: J. Hydrometeorol.
  doi: 10.1175/JHM-D-19-0298.1
– volume: 8
  start-page: 181
  year: 2020
  ident: 22130_CR47
  publication-title: Balk. J. Electr. Comput. Eng.
  doi: 10.17694/bajece.679662
– volume: 339
  start-page: 109579
  year: 2023
  ident: 22130_CR16
  publication-title: Agric. For. Meteorol.
  doi: 10.1016/j.agrformet.2023.109579
– volume: 275
  start-page: 108038
  year: 2023
  ident: 22130_CR20
  publication-title: Agric. Water Manag.
  doi: 10.1016/j.agwat.2022.108038
– volume: 23
  start-page: 3342
  year: 2009
  ident: 22130_CR59
  publication-title: Hydrol. Process.
  doi: 10.1002/hyp.7439
– volume: 38
  start-page: 389
  year: 2020
  ident: 22130_CR13
  publication-title: Irrig. Sci.
  doi: 10.1007/s00271-020-00680-5
– volume: 240
  start-page: 106210
  year: 2020
  ident: 22130_CR18
  publication-title: Agric. Water Manag.
  doi: 10.1016/j.agwat.2020.106210
– ident: 22130_CR40
– volume: 15
  start-page: 1149
  issue: 6
  year: 2023
  ident: 22130_CR68
  publication-title: Water
  doi: 10.3390/w15061149
– volume: 193
  start-page: 106687
  year: 2022
  ident: 22130_CR35
  publication-title: Comput. Electron. Agric.
  doi: 10.1016/j.compag.2022.106687
– volume: 13
  start-page: 609
  year: 2023
  ident: 22130_CR1
  publication-title: Agronomy
  doi: 10.3390/agronomy13020609
– volume: 4
  start-page: 149
  year: 2021
  ident: 22130_CR69
  publication-title: Water Energy Nexus
  doi: 10.1016/j.wen.2021.10.002
– volume: 215
  start-page: 132
  year: 2018
  ident: 22130_CR5
  publication-title: F. Crop. Res.
  doi: 10.1016/j.fcr.2017.10.009
– ident: 22130_CR50
  doi: 10.1007/978-3-030-89010-0_10
– start-page: 911
  volume-title: CIRP Encyclopedia of Production Engineering
  year: 2014
  ident: 22130_CR51
  doi: 10.1007/978-3-642-20617-7_6563
– volume: 14
  start-page: 256
  year: 2024
  ident: 22130_CR2
  publication-title: Appl. Water Sci.
  doi: 10.1007/s13201-024-02308-x
– volume: 297
  start-page: 107107
  year: 2024
  ident: 22130_CR71
  publication-title: Atmos. Res.
  doi: 10.1016/j.atmosres.2023.107107
– volume: 13
  start-page: 136
  year: 2023
  ident: 22130_CR23
  publication-title: Appl. Water Sci.
  doi: 10.1007/s13201-023-01942-1
– volume: 259
  start-page: 109314
  year: 2025
  ident: 22130_CR75
  publication-title: Catena
  doi: 10.1016/j.catena.2025.109314
– volume: 25
  start-page: 173
  year: 2023
  ident: 22130_CR15
  publication-title: J. Agrometeorol.
– volume: 103
  start-page: 831
  year: 2022
  ident: 22130_CR34
  publication-title: J. Inst. Eng. Ser. B
  doi: 10.1007/s40031-021-00684-5
– volume: 243
  start-page: 106454
  year: 2021
  ident: 22130_CR9
  publication-title: Agric. Water Manag.
  doi: 10.1016/j.agwat.2020.106454
– volume: 543
  start-page: 450
  year: 2016
  ident: 22130_CR54
  publication-title: J. Hydrol.
  doi: 10.1016/j.jhydrol.2016.10.022
– volume: 29
  start-page: 81279
  year: 2022
  ident: 22130_CR27
  publication-title: Environ. Sci. Pollut. Res.
  doi: 10.1007/s11356-022-21410-8
– volume: 13
  start-page: 2393
  year: 2021
  ident: 22130_CR24
  publication-title: Remote Sens.
  doi: 10.3390/rs13122393
– volume: 156
  start-page: 205
  year: 2025
  ident: 22130_CR39
  publication-title: Theor. Appl. Climatol.
  doi: 10.1007/s00704-025-05406-1
– volume: 14
  start-page: 241
  year: 2020
  ident: 22130_CR48
  publication-title: Front. Comput. Sci.
  doi: 10.1007/s11704-019-8208-z
– volume: 15
  start-page: 450
  year: 2023
  ident: 22130_CR17
  publication-title: Water
  doi: 10.3390/w15030450
– volume: 24
  start-page: 103135
  year: 2024
  ident: 22130_CR46
  publication-title: Results Eng.
  doi: 10.1016/j.rineng.2024.103135
– volume: 15
  start-page: 20179
  issue: 1
  year: 2025
  ident: 22130_CR79
  publication-title: Scientific Reports
  doi: 10.1038/s41598-025-05985-z
– volume: 9
  start-page: 123
  year: 2022
  ident: 22130_CR3
  publication-title: Hydrology
  doi: 10.3390/hydrology9070123
– volume: 54
  start-page: 945
  year: 2023
  ident: 22130_CR32
  publication-title: Hydrol. Res.
  doi: 10.2166/nh.2023.040
– ident: 22130_CR76
  doi: 10.1080/24749508.2024.2429842
– volume: 289
  start-page: 108499
  year: 2023
  ident: 22130_CR55
  publication-title: Agric. Water Manag.
  doi: 10.1016/j.agwat.2023.108499
– volume: 36
  start-page: 1025
  year: 2022
  ident: 22130_CR31
  publication-title: Water Resour. Manag.
  doi: 10.1007/s11269-022-03067-7
– volume: 53
  start-page: 5677
  year: 2023
  ident: 22130_CR61
  publication-title: IEEE Trans. Cybern.
  doi: 10.1109/TCYB.2022.3164542
– volume: 14
  start-page: 5771
  year: 2022
  ident: 22130_CR30
  publication-title: Sustainability
  doi: 10.3390/su14105771
– volume: 54
  start-page: 549
  year: 2023
  ident: 22130_CR19
  publication-title: Phys. Geogr. Res.
– volume: 59
  start-page: 102339
  year: 2025
  ident: 22130_CR73
  publication-title: J. Hydrol. Reg. Stud.
  doi: 10.1016/j.ejrh.2025.102339
– volume: 460
  start-page: 117440
  year: 2025
  ident: 22130_CR74
  publication-title: Geoderma
  doi: 10.1016/j.geoderma.2025.117440
– volume: 51
  start-page: 2945
  year: 2013
  ident: 22130_CR56
  publication-title: Desalin. Water Treat.
  doi: 10.1080/19443994.2012.748443
– volume: 155
  start-page: 4023
  issue: 5
  year: 2024
  ident: 22130_CR78
  publication-title: Theoretical and Applied Climatology
  doi: 10.1007/s00704-024-04862-5
– volume: 23
  start-page: 359
  year: 2013
  ident: 22130_CR4
  publication-title: J. Geogr. Sci.
  doi: 10.1007/s11442-013-1015-9
– ident: 22130_CR49
  doi: 10.1145/3018896.3065830
– volume: 38
  start-page: 343
  issue: 1
  year: 2024
  ident: 22130_CR77
  publication-title: Water Resources Management
  doi: 10.1007/s11269-023-03674-y
– volume: 154
  start-page: 110837
  year: 2023
  ident: 22130_CR64
  publication-title: China. Ecol. Indic.
  doi: 10.1016/j.ecolind.2023.110837
– volume: 261
  start-page: 107343
  year: 2022
  ident: 22130_CR33
  publication-title: Agric. Water Manag.
  doi: 10.1016/j.agwat.2021.107343
– volume: 16
  start-page: 246850
  year: 2025
  ident: 22130_CR41
  publication-title: Geomat. Nat. Hazards Risk
  doi: 10.1080/19475705.2025.2468850
– volume: 56
  start-page: 102043
  year: 2024
  ident: 22130_CR42
  publication-title: J. Hydrol. Reg. Stud.
  doi: 10.1016/j.ejrh.2024.102043
– volume-title: Physical Hydrology
  year: 2015
  ident: 22130_CR52
– year: 2022
  ident: 22130_CR66
  publication-title: Int. J. Environ. Res. Public Health
  doi: 10.3390/ijerph192013127
– volume-title: Study on the In-Field Water Balance and the Projected Impacts of Climate Change on Rice Yields in the Nile River Delta
  year: 2023
  ident: 22130_CR8
– ident: 22130_CR57
– volume: 591
  start-page: 125286
  year: 2020
  ident: 22130_CR72
  publication-title: J. Hydrol.
  doi: 10.1016/j.jhydrol.2020.125286
– volume: 572
  start-page: 556
  year: 2019
  ident: 22130_CR65
  publication-title: J. Hydrol.
  doi: 10.1016/j.jhydrol.2019.03.028
– volume: 536
  start-page: 376
  year: 2016
  ident: 22130_CR67
  publication-title: J. Hydrol.
  doi: 10.1016/j.jhydrol.2016.02.053
– volume: 87
  start-page: 2504
  year: 2023
  ident: 22130_CR70
  publication-title: Water Sci. Technol.
  doi: 10.2166/wst.2023.137
– volume: 67
  start-page: 1328
  year: 2022
  ident: 22130_CR29
  publication-title: Hydrol. Sci. J.
  doi: 10.1080/02626667.2022.2083511
– year: 2025
  ident: 22130_CR6
  publication-title: Irrig. Sci.
  doi: 10.1007/s00271-025-01019-8
– volume: 178
  start-page: 114012
  year: 2025
  ident: 22130_CR38
  publication-title: Ecol. Indic.
  doi: 10.1016/j.ecolind.2025.114012
– volume: 15
  start-page: 235
  year: 2024
  ident: 22130_CR44
  publication-title: Information
  doi: 10.3390/info15040235
– volume: 7
  start-page: 100483
  year: 2022
  ident: 22130_CR60
  publication-title: India. Environ. Chall.
  doi: 10.1016/j.envc.2022.100483
– volume: 35
  start-page: 14799
  year: 2023
  ident: 22130_CR28
  publication-title: Neural Comput. Appl.
  doi: 10.1007/s00521-023-08466-4
– volume: 4
  start-page: 714
  year: 2023
  ident: 22130_CR58
  publication-title: Brazil. Earth
  doi: 10.3390/earth4030038
– volume: 58
  start-page: e2021WR031069
  year: 2022
  ident: 22130_CR22
  publication-title: Water Resour. Res.
  doi: 10.1029/2021WR031069
– volume: 36
  start-page: 3483
  year: 2025
  ident: 22130_CR45
  publication-title: J. Intell. Manuf.
  doi: 10.1007/s10845-024-02405-3
SSID ssj0000529419
Score 2.4627047
Snippet The accurate estimation of actual evapotranspiration (AET) is crucial for sustainable water resource management, especially in water-scarce and agriculturally...
Abstract The accurate estimation of actual evapotranspiration (AET) is crucial for sustainable water resource management, especially in water-scarce and...
SourceID doaj
proquest
pubmed
crossref
springer
SourceType Open Website
Aggregation Database
Index Database
Publisher
StartPage 37548
SubjectTerms 704/106/242
704/106/694
704/172/4081
704/242
Agricultural hydrology
Agricultural water management
Artificial intelligence
Bayesian analysis
Bayesian optimization
Climate change
Climate data modeling
Data-driven evapotranspiration
Datasets
Drought
Environmental monitoring
Evapotranspiration
Humanities and Social Sciences
Irrigation scheduling
Learning algorithms
Machine learning
Mathematical models
multidisciplinary
Neural networks
Resource management
Science
Science (multidisciplinary)
Solar radiation
Vapor pressure
Water management
Water resources management
Wind speed
SummonAdditionalLinks – databaseName: DOAJ Directory of Open Access Journals
  dbid: DOA
  link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Ni9UwEA-yKHgRv62uEsGbls1Xk_ToiounxYPC3sKkTZZ32NdH293lefFfd5L0PVdUvHgrSQhJ5jedCZOZHyFvWKchRFTAJkSGFxQDtReC116zaBrfAPN9Jpswp6f27Kz9fIPqK70JK-WBy8Ed-cZziU59ANUpLQIYjTAK1gOoqHhMf19m2huXqVLVW7SKt0uWDJP2aEJLlbLJRFPjWiSrt79Yolyw_09e5m8R0mx4Tu6Te4vHSN-XlT4gt8L6IblTOCS3j8j3Y9iGlAlZD6j9F6tvoacX-YVkoAslxDlFV3qaJwo5W4SGK9gMc65qvioAoJsxBWzy52pNr9EBHeuSRoLTwfm4r9BBE5MDIpUOkWbu7cfk68nHLx8-1QurQt0paefaShU5eNaZ1nCutWJgEn8V63wU3jfBmA5AaAkMtPEQess8FwxUaHpAQ_eEHKyHdXhGqBBtb4wPIGJUUWrbReaDZh69Ctt3siJvdyfsNqV4hstBb2ldkYdDebgsD7etyHESwn5kKnydGxAOboGD-xccKnK4E6FbtHFyMnkhaIZbXpHX-27UoxQcgXUYLssYvKqypqnI0yL6_UoUTyxRFnve7bDwc_K_b-j5_9jQC3JXJNCiuRT2kBzM42V4SW53V_NqGl9l1P8AgXIHzw
  priority: 102
  providerName: Directory of Open Access Journals
Title Bayesian-optimized machine learning boosts actual evapotranspiration prediction in water-stressed agricultural regions of China
URI https://link.springer.com/article/10.1038/s41598-025-22130-y
https://www.ncbi.nlm.nih.gov/pubmed/41152585
https://www.proquest.com/docview/3266184791
https://www.proquest.com/docview/3266448055
https://doaj.org/article/b5b13716ea4c462ea76510e8baa4f41f
Volume 15
WOSCitedRecordID wos001604676500028&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ
  customDbUrl:
  eissn: 2045-2322
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000529419
  issn: 2045-2322
  databaseCode: DOA
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2045-2322
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000529419
  issn: 2045-2322
  databaseCode: M~E
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
– providerCode: PRVPQU
  databaseName: Biological Science Database
  customDbUrl:
  eissn: 2045-2322
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000529419
  issn: 2045-2322
  databaseCode: M7P
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/biologicalscijournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest - Health & Medical Complete保健、医学与药学数据库
  customDbUrl:
  eissn: 2045-2322
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000529419
  issn: 2045-2322
  databaseCode: 7X7
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/healthcomplete
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest - Publicly Available Content Database
  customDbUrl:
  eissn: 2045-2322
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000529419
  issn: 2045-2322
  databaseCode: PIMPY
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 2045-2322
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000529419
  issn: 2045-2322
  databaseCode: BENPR
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Science Database
  customDbUrl:
  eissn: 2045-2322
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000529419
  issn: 2045-2322
  databaseCode: M2P
  dateStart: 20110101
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/sciencejournals
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELboLki98C5dKCsjcYOojuPEzgmxqBUcuooQSMspGif2ag9NliQtWi78dcZONhXiceFiRbEVOZqnZzzzEfKSFQkYiwIYG8vwgCIh0JyHgU6YlbGOgenSg03I5VKtVmk2BNza4VrlXid6RV3WhYuRn0bOkqAqTcM326-BQ41y2dUBQuOATF2nMj4h08XZMvs4RllcHkuE6VAtwyJ12qLFclVlPA5wTxELdr9YJN-4_0_e5m-ZUm-Azu_979bvk7uD60nf9rzygNwy1UNypwej3D0iPxawM66kMqhRjVxuvpuSXvqrloYO2BJrij5527UUfNkJNdewrTvfHn3TcxLdNi7z4x83Ff2GnmwT9PUo-DlYN2OrD-ogIZDlaW2pB_F-TD6fn3169z4Y4BmCQkSqC1QkbAiaFTKVYZgkgoF0QFis0JZrHRspCwCeRMAgkRpMqZgOOQNh4hLQYh6RSVVX5phQztNSSm2AWytslKjCMm0SptE9UWURzcirPYnybd-FI_fZ80jlPUFzJGjuCZrvZmThqDiudB20_Yu6WeeDQOY61mGEh0UDohAJNyATVE9GaQBhRWhn5GRPzHwQ6za_oeSMvBinUSBdlgUqU1_1a_DMy-J4Rp70vDPuRIQObkrhzOs9M918_O8_9PTfe3lGDrnjZ7SoXJ2QSddcmefkdnHdbdpmTg7kSvpRzQfxmPvIA44XPHOjxHGafbjIvvwE60sdPQ
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELaqLQguvKELBYwEJ4jqV2LvASEWWrVqWVWoSL2ZcWKv9tBkm2xbLRf-Eb8R20m2QjxuPXCLkiiyk28-z2Q88yH0kuQZWOcNMLWO-ABFQmIYo4nJiJOpSYGYIopNyMlEHR-PDtfQj74WJmyr7DkxEnVR5eEf-RYPK4mn0hF9Nz9NgmpUyK72EhotLPbt8sKHbM3bvY_--75ibGf76MNu0qkKJLngapEoLhwFQ3I5kpRmmSAgg34TyY1jxqRWyhyAZRwIZNKALRQxlBEQNi2AhkYHnvLXBRepGKD18fbk8PPqr07Imwk66qpzCFdbjV8hQxUbSxP_DjhJlr-sgFEo4E_e7W-Z2bjg7dz-317VHXSrc63x-9YW7qI1W95D11uxzeV99H0MSxtKRpPK0-TJ7Jst8EncSmpxp50xxT7maBYNhlhWg-05zKtFbP8-ay0Fz-uQ2YqHsxJfeE-9Ttp6G_84mNarViY4SF54k8aVw1Gk_AH6ciWzf4gGZVXaDYQZGxVSGgvMOeF4pnJHjM2I8e6XKnI-RK97SOh522VEx90BXOkWQNoDSEcA6eUQjQNqVneGDuHxRFVPdUc42qSGch8MWxC5yJgFmXn6tcoACCeoG6LNHjy6o61GXyJniF6sLnvCCVkkKG111t7jY3qSpkP0qMXqaiSCBjkt5a-86cF7-fC_T-jxv8fyHN3YPfp0oA_2JvtP0E0WbMl7D0xtosGiPrNP0bX8fDFr6medOWL09aph_RPvXXOV
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwELWqLSAu5RsWChgJThCt7Tix94AQpaxYFVZ7AKmc0nFir_bQzTbZtlou_C9-HWMn2QrxceuBW5REkZ28eZ7JeOYR8pzlKViHBphYxzBAURAZIXhkUuZUYhJgpghiE2oy0YeHw-kW-dHVwvhtlR0nBqIuytz_Ix_EfiVBKh3ygWu3RUz3R2-WJ5FXkPKZ1k5Oo4HIgV2fY_hWvx7v47d-IcTo_ed3H6JWYSDKZaxXkY6l42BYroaK8zSVDJTXcmK5ccKYxCqVA4g0BgapMmALzQwXDKRNCuC-6QHS_za65EL2yPZ0_Gn6dfOHx-fQJB-2lTos1oMaV0tf0SaSCN9HzKL1L6thEA34k6f7W5Y2LH6jG__za7tJdlqXm75tbOQW2bKL2-RqI8K5vkO-78Ha-lLSqET6PJ5_swU9DltMLW01NWYUY5F6VVMI5TbUnsGyXIW28PPGguiy8hmvcDhf0HP04KuoqcPBx8Gs2rQ4oV4KA02dlo4G8fK75MulzP4e6S3KhX1AqBDDQiljQTgnXZzq3DFjU2bQLdNFHvfJyw4e2bLpPpKFXQOxzhowZQimLIApW_fJnkfQ5k7fOTycKKtZ1hJRZhLDYwySLchcpsKCSpGWrTYA0knu-mS3A1LW0lmdXaCoT55tLiMR-ewSLGx52tyDsT5Lkj653-B2MxLJvcyWxiuvOiBfPPzvE3r477E8JdcQy9nH8eTgEbkuvFmhUyH0LumtqlP7mFzJz1bzunrSWiYlR5eN6p-i_3yH
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Bayesian-optimized+machine+learning+boosts+actual+evapotranspiration+prediction+in+water-stressed+agricultural+regions+of+China&rft.jtitle=Scientific+reports&rft.au=Elbeltagi%2C+Ahmed&rft.au=Srivastava%2C+Aman&rft.au=Cao%2C+Xinchun&rft.au=Gautam%2C+Vinay+Kumar&rft.date=2025-10-28&rft.pub=Nature+Publishing+Group+UK&rft.eissn=2045-2322&rft.volume=15&rft.issue=1&rft_id=info:doi/10.1038%2Fs41598-025-22130-y&rft.externalDocID=10_1038_s41598_025_22130_y
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon