Crown Profile Modeling and Prediction Based on Ensemble Learning
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...
Main Authors: | Yuling Chen, Chen Dong, Baoguo Wu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-03-01
|
Series: | Forests |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-4907/13/3/410 |
Similar Items
-
Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model
by: Alin-Ionuț Pleșoianu, et al.
Published: (2020-07-01) -
Deep learning for crown profile modelling of Pinus yunnanensis secondary forests in Southwest China
by: Yuling Chen, et al.
Published: (2023-02-01) -
Comparison of the Marginal Fit of Heat Pressed Crowns Fabricated With 3D Printed and Conventional Methods
by: Fereshteh Seyedeh, et al.
Published: (2022-01-01) -
A Crown Contour Envelope Model of Chinese Fir Based on Random Forest and Mathematical Modeling
by: Yingze Tian, et al.
Published: (2020-12-01) -
SEM Evaluation of Marginal Adaptation E-Max Crowns Manufactured by Printing-Pressed and Milling
by: Ana Ispas, et al.
Published: (2023-11-01)