An application of R Software model based on Deep Learning Algorithms to future usage of other forest practitioner for predicting individual tree height

Year 2019, Volume: 5 Issue: 2, 117 - 126, 31.12.2019

İlker Ercanlı

Abstract

In this study, the artificial intelligence models
based on Deep learning Algorithms were developed to model the relationships
between the individual tree total heights (ITH) and diameter at breast heights (DBH)
with the stand variables. The H20 package, which have been coded in R software
language, with an h2o.deeplearning
function, which was coded in Java, was used to train these DLA models and obtain the ITH predictions. To determine best
predictive input variables, various input variable alternatives were evaluated
based on the statistical fitting criteria. From these fitting statistics for the training data set, the DLA
model which includes the input variables with the DBH, dominant diameter (cm), dominant height (m), number of trees in
hectare and basal area (m²/ha) resulted in the best
predictive statistics with a RMSE value of 0.7173, RMSE% value of 4.5986, the
AIC value of -291.3037, BIC value of 1158.4564, FI of 0.9785 values, AAE value
of 0.4311, Bias value of 0.0438 and Bias% value of 0.2805. Similar to the
fitting statistics in training data, the DLA model which includes the input
variables with the DBH, dominant diameter (cm), dominant height (m), number of
trees in hectare and basal area (m2/ha) gave the best predictive statistics
with a RMSE value of 1.8217, RMSE% value of 10.2151, the AIC value of 99.9615,
BIC value of 331.3772, FI of 0.8334 values, AAE value of 1.2051, Bias value of
-0.0985 and Bias% value of -0.5521. To
train these DLA models, R software platform, which is free and open for all,
was used to share with various stakeholders and other users in forest
management. Thus, besides the modeling studies including the comparison of
various network models with classical regression models, the opportunity
to other forest practitioner to use artificial intelligence
model developed in this study can be achieved by downloading this best
predictive DLA model from the supplementary file section
of this study.   

Keywords

Individual Tree Height, Deep Learning Algorithms, H20 package, h2o.deeplearning function

References

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