Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height
| Autor: | İlker Ercanli |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Artificial intelligence Mean squared error 01 natural sciences Approximation error Bayesian information criterion lcsh:QH540-549.5 Statistics Ecology Evolution Behavior and Systematics Nature and Landscape Conservation Mathematics Ecology Artificial neural network Individual tree height Diameter at breast height Forestry 04 agricultural and veterinary sciences Deep learning algorithms Tree (data structure) 040103 agronomy & agriculture 0401 agriculture forestry and fisheries lcsh:Ecology Akaike information criterion Prediction Nonlinear regression 010606 plant biology & botany |
| Zdroj: | Forest Ecosystems, Vol 7, Iss 1, Pp 1-18 (2020) |
| ISSN: | 2197-5620 |
| Popis: | Background Deep Learning Algorithms (DLA) have become prominent as an application of Artificial Intelligence (AI) Techniques since 2010. This paper introduces the DLA to predict the relationships between individual tree height (ITH) and the diameter at breast height (DBH). Methods A set of 2024 pairs of individual height and diameter at breast height measurements, originating from 150 sample plots located in stands of even aged and pure Anatolian Crimean Pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) in Konya Forest Enterprise. The present study primarily investigated the capability and usability of DLA models for predicting the relationships between the ITH and the DBH sampled from some stands with different growth structures. The 80 different DLA models, which involve different the alternatives for the numbers of hidden layers and neuron, have been trained and compared to determine optimum and best predictive DLAs network structure. Results It was determined that the DLA model with 9 layers and 100 neurons has been the best predictive network model compared as those by other different DLA, Artificial Neural Network, Nonlinear Regression and Nonlinear Mixed Effect models. The alternative of 100 # neurons and 9 # hidden layers in deep learning algorithms resulted in best predictive ITH values with root mean squared error (RMSE, 0.5575), percent of the root mean squared error (RMSE%, 4.9504%), Akaike information criterion (AIC, − 998.9540), Bayesian information criterion (BIC, 884.6591), fit index (FI, 0.9436), average absolute error (AAE, 0.4077), maximum absolute error (max. AE, 2.5106), Bias (0.0057) and percent Bias (Bias%, 0.0502%). In addition, these predictive results with DLAs were further validated by the Equivalence tests that showed the DLA models successfully predicted the tree height in the independent dataset. Conclusion This study has emphasized the capability of the DLA models, novel artificial intelligence technique, for predicting the relationships between individual tree height and the diameter at breast height that can be required information for the management of forests. |
| Databáze: | OpenAIRE |
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