The Predicting Tree Growth App: an algorithmic approach to modelling individual tree growth

Juliana G. de S. Magalhaes, Adam P. Polinko, Mariano M. Amoroso, Gursimran S. Kohli and Bruce C. Larson

Ecological Modelling, 2022, vol. 467, issue C

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.

Keywords: Individual tree growth modelling; Machine learning algorithms; Recurrent neural network; Software (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:467:y:2022:i:c:s0304380022000552

DOI: 10.1016/j.ecolmodel.2022.109932

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