Mining and quantifying the optimal DBH range of loblolly pine with improved particle algorithm

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Dongsheng Qing, Jianjun Li, Qiaoling Deng, Shuai Liu
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引用次数: 0

Abstract

In order to fully understand the objective law of height and DBH growth of loblolly pine trees and exploring the best DBH (Diameter at Breast Height) Range for loblolly pine tree height growth, 13 340 loblolly pines with initial DBH between 1 inch and 7 inch were selected from Alabama as research objects, and statistics on its growth from 2000 to 2015. Because particle swarm optimization (PSO) is suitable for solving non-linear problems, the optimal DBH of loblolly pine is transformed into the optimization problem of PSO, which quantifies the optimal DBH range of loblolly pine at different scales by mapping strategy. The experimental results show that the range of the breast diameter suitable for the high growth of the pine tree is concentrated between 3.7 inch and 7.3 inch. The height of the pine tree begins to enter a period of rapid growth from a breast diameter of 3.9 inch (ą0.2 inch ). The tree height growth rate reached a maximum at a breast diameter of 6.4 inch (ą0.6 inch ), and the tree height entered a slow growth period after the breast diameter of 11.92 inch (ą0.3 inch). In general, when the breast diameter exceeds 15.26 inch (ą0.3 inch), the height of the pine tree stops growing.
利用改进的粒子算法挖掘和量化火炬松最优胸径范围
为充分了解火炬松林木高度和胸径生长的客观规律,探索火炬松林木高度生长的最佳胸径范围,选取美国阿拉巴马州13340株初始胸径在1 ~ 7寸之间的火炬松作为研究对象,统计2000 ~ 2015年火炬松的生长情况。由于粒子群算法适用于求解非线性问题,将火炬松的最优胸径转化为粒子群算法的优化问题,通过映射策略量化不同尺度下火炬松的最优胸径范围。实验结果表明,适合高生长松树的胸径范围集中在3.7 ~ 7.3英寸之间。松树的高度从胸径3.9英寸(ą0.2英寸)开始进入快速生长期。树高增长率在胸径6.4英寸(ą0.6英寸)时达到最大值,胸径11.92英寸(ą0.3英寸)后树高进入缓慢生长期。一般来说,当胸径超过15.26英寸(ą0.3英寸)时,松树的高度就会停止生长。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neural Network World
Neural Network World 工程技术-计算机:人工智能
CiteScore
1.80
自引率
0.00%
发文量
0
审稿时长
12 months
期刊介绍: Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.
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