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dc.contributor.author
Magalhaes, Juliana G. de S.  
dc.contributor.author
Polinko, Adam P.  
dc.contributor.author
Amoroso, Mariano Martin  
dc.contributor.author
Kohli, Gursimran S.  
dc.contributor.author
Larson, Bruce C.  
dc.date.available
2023-03-17T10:35:37Z  
dc.date.issued
2022-05  
dc.identifier.citation
Magalhaes, Juliana G. de S.; Polinko, Adam P.; Amoroso, Mariano Martin; Kohli, Gursimran S.; Larson, Bruce C.; The Predicting Tree Growth App: an algorithmic approach to modelling individual tree growth; Elsevier Science; Ecological Modelling; 467; 109932; 5-2022; 1-5  
dc.identifier.issn
0304-3800  
dc.identifier.uri
http://hdl.handle.net/11336/190854  
dc.description.abstract
PredictingTreeGrowth is free and open-source application software written in Python 3.7 that allows easy and fast development of predictive models using the Recurrent Neural Network (RNN)/Long Short-Term Memory (LSTM) framework. RNNs have an upgraded architecture able to capture tree growth mechanisms related to time ordering and size dependence. The motivation for this App is to demystify the use of Machine Learning algorithms and allow accessibility of Machine Learning algorithms by the scientific community. Its simple graphical user interface (GUI) provides straightforward tools for building predictive models with the RNN algorithm.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Elsevier Science  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
INDIVIDUAL TREE GROWTH MODELLING  
dc.subject
MACHINE LEARNING ALGORITHMS  
dc.subject
RECURRENT NEURAL NETWORK  
dc.subject
SOFTWARE  
dc.subject.classification
Silvicultura  
dc.subject.classification
Agricultura, Silvicultura y Pesca  
dc.subject.classification
CIENCIAS AGRÍCOLAS  
dc.title
The Predicting Tree Growth App: an algorithmic approach to modelling individual tree growth  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2023-03-03T17:02:24Z  
dc.journal.volume
467  
dc.journal.number
109932  
dc.journal.pagination
1-5  
dc.journal.pais
Países Bajos  
dc.journal.ciudad
Amsterdam  
dc.description.fil
Fil: Magalhaes, Juliana G. de S.. University of British Columbia; Canadá  
dc.description.fil
Fil: Polinko, Adam P.. Mississippi State University.; Estados Unidos  
dc.description.fil
Fil: Amoroso, Mariano Martin. Universidad Nacional de Río Negro. Sede Andina. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Kohli, Gursimran S.. University Fraser Simon; Canadá  
dc.description.fil
Fil: Larson, Bruce C.. University of British Columbia; Canadá  
dc.journal.title
Ecological Modelling  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1016/j.ecolmodel.2022.109932  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0304380022000552  

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