Crown Profile Modeling and Prediction Based on Ensemble Learning

Abstract

Improving prediction accuracy is a prominent modeling issue in relation to forest simulations, and ensemble learning is a new effective method for improving the precision of crown profile model simulations in order to overcome the disadvantages of statistical modeling. Background: Ensemble learning (a machine learning paradigm in which multiple learners are trained to achieve better performance) has strong nonlinear problem learning ability and flexibility in terms of analyzing longitudinal data, and it remains rarely explored so far in the field of crown profile modeling forest science. In this study, we explored the application of ensemble learning to the modeling and prediction of crown profiles. Methods: We evaluated the performance of ensemble learning procedures and marginal model in modeling crown profile using the crown profile database from China fir plantations in Fujian, in southern China. Results: The ensemble learning approach for the crown profile model appeared to have better performance and higher efficiency (R2 > 0.9). The crown equation model 18 showed an intermediate performance in its estimation, whereas GBDT (MAE = 0.3250, MSE = 0.2450) appeared to have the best performance and higher efficiency. Conclusions: The ensemble learning method can combine the advantages of multiple learners and has higher model accuracy, robustness and overall induction ability, and is thus an effective technique for crown profile modeling and prediction.

Image Analysis Method of Substation Equipment Based on Convolutional Neural Network

“…When the number of iterations is set to 9, the recognition accuracy of the model is the best and the running time at this time has less impact on the model. e main idea of integrated learning [21,22] is to build three neural network structures with high performance and diversity and to combine the three learners to recognise images through a voting strategy. High performance means that each neural network has a relatively high recognition rate, without using multiple "weak learners" for learning.…”

Prediction of Road Transport of Wood in Uruguay: Approach with Machine Learning

“…To create a balanced dataset, the oversampling technique was applied [39]. Using the random forests algorithm [40], the importance of the attributes for the modeling process was observed and, through the spearman correlation [41], the parameters correlated with 5% of significance were excluded.…”

Deep learning for crown profile modelling of Pinus yunnanensis secondary forests in Southwest China

“…It has been of interest for forest modelers to better understand this phenomenon, particularly on the basis of statistical modeling and analysis (Wang et al, 2017). The traditional crown profile modeling methods have been used to deal with the autocorrelation and heteroscedasticity in the crown profile equations, they are mainly direct variance-covariance modelling (Hann, 1999;Crecente-Campo et al, 2009;Crecente-Campo et al, 2013), mixed-effects modelling (Fu et al, 2013;Sharma et al, 2016;Fu et al, 2017;Sharma et al, 2017;Sun et al, 2017;Jia and Chen, 2019;Wang et al, 2019;Chen et al, 2021;Di Salvatore et al, 2021), and nonlinear marginal modeling (McCulloch and Searle, 2001;Lejeune et al, 2009;de-Miguel et al, 2012;Chen et al, 2022). With the rapid development of machine learning artificial intelligence, some machine learning algorithms have the characteristics of high accuracy and good robustness for the data with nonlinear features (Singh et al, 2016;Dong et al, 2021), which has subsequently been applied to crown profile modeling.…”