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Journals / Anadolu Orman Araştırmaları Dergisi / 2019 / Cilt: 5 Sayı: 2

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

İlker Ercanlı(ÇANKIRI KARATEKİN ÜNİVERSİTESİ)0000-0003-4250-7371
Pages
117–126
Publication Date
2019-12-31
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

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Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:2051 Verileri
⚙️Variables:Individual Tree Height · Deep Learning Algorithms · H20 package

Özet

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 (m 2 /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.

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Cite This Article

Ercanlı, İ. (2019). An application of R Software model based on Deep Learning Algorithms to future usage of other forest practitioner for predicting individual tree height. Anadolu Orman Araştırmaları Dergisi, Cilt: 5 Sayı: 2, 117–126. https://ajindex.com/articles/1360860

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