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Refinado por: Base de dados/Biblioteca: IOP Publishing (Open access) remover assunto: Learning Algorithms remover
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1
Machine learning and artificial intelligence to aid climate change research and preparedness
Machine learning and artificial intelligence to aid climate change research and preparedness
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Machine learning and artificial intelligence to aid climate change research and preparedness

Huntingford, Chris ; Jeffers, Elizabeth S ; Bonsall, Michael B ; Christensen, Hannah M ; Lees, Thomas ; Yang, Hui

Environmental research letters, 2019-12, Vol.14 (12), p.124007 [Periódico revisado por pares]

Bristol: IOP Publishing

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2
Machine learning methods for crop yield prediction and climate change impact assessment in agriculture
Machine learning methods for crop yield prediction and climate change impact assessment in agriculture
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Machine learning methods for crop yield prediction and climate change impact assessment in agriculture

Crane-Droesch, Andrew

Environmental research letters, 2018-10, Vol.13 (11), p.114003 [Periódico revisado por pares]

Bristol: IOP Publishing

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3
How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions
How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions
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How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Sun, Alexander Y ; Scanlon, Bridget R

Environmental research letters, 2019-07, Vol.14 (7), p.73001 [Periódico revisado por pares]

Bristol: IOP Publishing

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4
A global map of mangrove forest soil carbon at 30 m spatial resolution
A global map of mangrove forest soil carbon at 30 m spatial resolution
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A global map of mangrove forest soil carbon at 30 m spatial resolution

Sanderman, Jonathan ; Hengl, Tomislav ; Fiske, Greg ; Solvik, Kylen ; Adame, Maria Fernanda ; Benson, Lisa ; Bukoski, Jacob J ; Carnell, Paul ; Cifuentes-Jara, Miguel ; Donato, Daniel ; Duncan, Clare ; Eid, Ebrahem M ; Ermgassen, Philine zu ; Lewis, Carolyn J Ewers ; Macreadie, Peter I ; Glass, Leah ; Gress, Selena ; Jardine, Sunny L ; Jones, Trevor G ; Nsombo, Eugéne Ndemem ; Rahman, Md Mizanur ; Sanders, Christian J ; Spalding, Mark ; Landis, Emily

Environmental research letters, 2018-05, Vol.13 (5), p.55002 [Periódico revisado por pares]

Bristol: IOP Publishing

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5
Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
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Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US

Konapala, Goutam ; Kao, Shih-Chieh ; Painter, Scott L ; Lu, Dan

Environmental research letters, 2020-10, Vol.15 (10), p.104022 [Periódico revisado por pares]

Bristol: IOP Publishing

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6
Evaluation and machine learning improvement of global hydrological model-based flood simulations
Evaluation and machine learning improvement of global hydrological model-based flood simulations
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Evaluation and machine learning improvement of global hydrological model-based flood simulations

Yang, Tao ; Sun, Fubao ; Gentine, Pierre ; Liu, Wenbin ; Wang, Hong ; Yin, Jiabo ; Du, Muye ; Liu, Changming

Environmental research letters, 2019-11, Vol.14 (11), p.114027 [Periódico revisado por pares]

Bristol: IOP Publishing

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7
Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt
Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt
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Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt

Wolanin, Aleksandra ; Mateo-García, Gonzalo ; Camps-Valls, Gustau ; Gómez-Chova, Luis ; Meroni, Michele ; Duveiller, Gregory ; Liangzhi, You ; Guanter, Luis

Environmental research letters, 2020-02, Vol.15 (2), p.24019 [Periódico revisado por pares]

Bristol: IOP Publishing

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8
Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models
Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models
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Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models

Leng, Guoyong ; Hall, Jim W

Environmental research letters, 2020-04, Vol.15 (4), p.044027 [Periódico revisado por pares]

England: IOP Publishing

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9
No perfect storm for crop yield failure in Germany
No perfect storm for crop yield failure in Germany
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No perfect storm for crop yield failure in Germany

Webber, Heidi ; Lischeid, Gunnar ; Sommer, Michael ; Finger, Robert ; Nendel, Claas ; Gaiser, Thomas ; Ewert, Frank

Environmental research letters, 2020-10, Vol.15 (10), p.104012 [Periódico revisado por pares]

Bristol: IOP Publishing

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10
Leveraging machine learning for predicting flash flood damage in the Southeast US
Leveraging machine learning for predicting flash flood damage in the Southeast US
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Leveraging machine learning for predicting flash flood damage in the Southeast US

Alipour, Atieh ; Ahmadalipour, Ali ; Abbaszadeh, Peyman ; Moradkhani, Hamid

Environmental research letters, 2020-02, Vol.15 (2), p.24011 [Periódico revisado por pares]

Bristol: IOP Publishing

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