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Artificial neural networks: Modeling tree survival and mortality in the Atlantic Forest biome in Brazil
Science of the Total Environment ( IF 8.2 ) Pub Date : 2018-07-18 , DOI: 10.1016/j.scitotenv.2018.07.123
Samuel José Silva Soares da Rocha , Carlos Moreira Miquelino Eleto Torres , Laércio Antônio Gonçalves Jacovine , Helio Garcia Leite , Eduardo Monteiro Gelcer , Karina Milagres Neves , Bruno Leão Said Schettini , Paulo Henrique Villanova , Liniker Fernandes da Silva , Leonardo Pequeno Reis , José Cola Zanuncio

Models to predict tree survival and mortality can help to understand vegetation dynamics and to predict effects of climate change on native forests. The objective of the present study was to use Artificial Neural Networks, based on the competition index and climatic and categorical variables, to predict tree survival and mortality in Semideciduous Seasonal Forests in the Atlantic Forest biome. Numerical and categorical trees variables, in permanent plots, were used. The Agricultural Reference Index for Drought (ARID) and the distance-dependent competition index were the variables used. The overall efficiency of classification by ANNs was higher than 92% and 93% in the training and test, respectively. The accuracy for classification and number of surviving trees was above 99% in the test and in training for all ANNs. The classification accuracy of the number of dead trees was low. The mortality accuracy rate (10.96% for training and 13.76% for the test) was higher with the ANN 4, which considers the climatic variable and the competition index. The individual tree-level model integrates dendrometric and meteorological variables, representing a new step for modeling tree survival in the Atlantic Forest biome.



中文翻译:

人工神经网络:模拟巴西大西洋森林生物群落中的树木成活率和死亡率

预测树木成活率和死亡率的模型可以帮助理解植被动态并预测气候变化对原生林的影响。本研究的目的是基于竞争指数以及气候和分类变量,使用人工神经网络来预测大西洋森林生物群落中半落叶季节性森林的树木存活率和死亡率。在永久性地块中使用了数字和分类树变量。农业干旱参考指数(ARID)和依赖于距离的竞争指数是所使用的变量。在训练和测试中,人工神经网络进行分类的总体效率分别高于92%和93%。在测试和所有人工神经网络的训练中,存活树木的分类和数量的准确性均高于99%。死树数量的分类精度低。ANN 4考虑了气候变量和竞争指数,死亡率准确率较高(训练为10.96%,测试为13.76%)更高。单个树级模型集成了树木学和气象变量,代表了在大西洋森林生物群系中树种生存建模的新步骤。

更新日期:2018-07-19
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