期刊
ENVIRONMENTAL REVIEWS
卷 26, 期 4, 页码 339-350出版社
CANADIAN SCIENCE PUBLISHING
DOI: 10.1139/er-2018-0034
关键词
decision-trees learning; artificial neural network; support vector machine; species classification; hazard assessment; forest management
资金
- Fonds de recherche du Quebec (FQRNT) program
- Natural Sciences and Engineering Research Council of Canada (NSERC)
Machine learning, an important branch of artificial intelligence, is increasingly being applied in sciences such as forest ecology. Here, we review and discuss three commonly used methods of machine learning (ML) including decision-tree learning, artificial neural network, and support vector machine and their applications in four different aspects of forest ecology over the last decade. These applications include: (i) species distribution models, (ii) carbon cycles, (iii) hazard assessment and prediction, and (iv) other applications in forest management. Although ML approaches are useful for classification, modeling, and prediction in forest ecology research, further expansion of ML technologies is limited by the lack of suitable data and the relatively higher threshold of applications. However, the combined use of multiple algorithms and improved communication and cooperation between ecological researchers and ML developers still present major challenges and tasks for the betterment of future ecological research. We suggest that future applications of ML in ecology will become an increasingly attractive tool for ecologists in the face of big data and that ecologists will gain access to more types of data such as sound and video in the near future, possibly opening new avenues of research in forest ecology.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据