Abstract
BackgroundDeep 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).MethodsA 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.ResultsIt 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.ConclusionThis 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.
Highlights
The significant components of forest inventory, which is the first phase of forest planning, are the measurement of the individual tree heights (ITH) and the diameter at breast height (DBH)
Considering the predictive capability of ITH obtained by these Deep Learning Algorithms (DLA) models, it can be observed that the DLA model with 9 layers and 100 neurons produced higher prediction precisions than those by the Nonlinear Regression Models (NLRM), Nonlinear Mixed Effect (NLME) and FFBANN and Cascade Correlation (CC)-Artificial Neural Network (ANN) (Fig. 2), which this DLA model gave the tree height predictions that were very close to the observed ones
This paper presents the DLA models, as innovative prediction technique, to predict the relationships between individual tree heights and diameter at breast height, which are an important growth parameter of trees and so, the usability and capability of the DLA were evaluated based on some fitting criteria in both training and simulation datasets
Summary
The significant components of forest inventory, which is the first phase of forest planning, are the measurement of the individual tree heights (ITH) and the diameter at breast height (DBH). As a more common approach, the multivariate nonlinear regression models which comprise various stand attributes such as stand basal area, site index, stand age or stocking index in addition to the DBH were developed by various studies such as Huang et al (2000), Sharma and Zhang (2004), Temesgen and Gadow (2004), Dorado et al (2005), Trincado et al (2007), Adame et al (2008), Paulo et al (2011). These multivariate ITH models with supplemental stand attributes are called as “generalized heightdiameter models”. This paper introduces the DLA to predict the relationships between individual tree height (ITH) and the diameter at breast height (DBH)
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