Artículo
The Predicting Tree Growth App: an algorithmic approach to modelling individual tree growth
Magalhaes, Juliana G. de S.; Polinko, Adam P.; Amoroso, Mariano Martin
; Kohli, Gursimran S.; Larson, Bruce C.
![Icon](/themes/CONICETDigital/images/conicet.png)
Fecha de publicación:
05/2022
Editorial:
Elsevier Science
Revista:
Ecological Modelling
ISSN:
0304-3800
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
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Articulos (IRNAD)
Articulos de INSTITUTO DE INVESTIGACIONES EN RECURSOS NATURALES, AGROECOLOGIA Y DESARROLLO RURAL
Articulos de INSTITUTO DE INVESTIGACIONES EN RECURSOS NATURALES, AGROECOLOGIA Y DESARROLLO RURAL
Citación
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
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