Research Article | OPEN ACCESS
Leaf Mass Estimation Affected by Leaf Shape and Tree Height based on Simulation and Four-Layer Hidden-node Neural Network
1Zi-Yue Chen, 2Yuan-Biao Zhang, 3Zhao-Wei Wang and 4Qiu-Ye Qian
1International Business School
2Mathematical Modeling Innovative Practice Base
Packaging Engineering Institute
Key Laboratory of Product Packaging and Logistics of Guangdong Higher Education Institutes
3Electrical Information College
4International Business School, Jinan University, Zhuhai 519070, China
Research Journal of Applied Sciences, Engineering and Technology 2013 16:4142-4148
Received: July 31, 2012 | Accepted: September 08, 2012 | Published: April 30, 2013
Abstract
Leaf mass, a vital parameter of forest ecology and physiology, is estimated in this study. According to the simulation result in this study, leaf shape makes little contribution to the exposure area. Moreover, a four-layer hidden-node neural network model is used in the new model, analyzing correlation between leaf mass and height of trees. Leaves of various areas are tested as sensitivity analysis for the simulation which studied the influence of leaf shape on exposure area. Finally, leaf mass is estimated by a statistical model based on the regression relationship of sapwood area and leaf mass.
Keywords:
Four -layer hidden-node neural network model, leaf mass, shape, simulation,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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