{"title":"BP Neural Network Based Prediction of Potential Mikania micrantha Distribution in Guangzhou City","authors":"L. Qiu, D. Zhang, H. Huang, Q. Xiong, G. Zhang","doi":"10.4172/2168-9776.1000216","DOIUrl":null,"url":null,"abstract":"To predict the distribution of Mikania micrantha, one of the most harmful invasive plants in Guangzhou City, the author selected relevant environmental factors and established a feasible simple model based on BP neural network to use its strong nonlinear ability in this paper. From this model, it is concluded that the distribution possibility of Mikania micrantha in Liwan District, Yuexiu District and Haizhu District is near 0, which are classified as regions without invasion risk; the distribution possibility in Conghua District and Huadu District is 60% and 69.3% respectively, which are defined as regions with low invasion risk; the distribution possibility in Baiyun District, Panyu District, Zengcheng District and Nansha District are much higher, which are identified as regions with high invasion risk; while the distribution possibility in Luogang District, Tianhe District and Huangpu District are the highest, which are determined as regions with highest risk.","PeriodicalId":35920,"journal":{"name":"林业科学研究","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"林业科学研究","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.4172/2168-9776.1000216","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
引用次数: 1
Abstract
To predict the distribution of Mikania micrantha, one of the most harmful invasive plants in Guangzhou City, the author selected relevant environmental factors and established a feasible simple model based on BP neural network to use its strong nonlinear ability in this paper. From this model, it is concluded that the distribution possibility of Mikania micrantha in Liwan District, Yuexiu District and Haizhu District is near 0, which are classified as regions without invasion risk; the distribution possibility in Conghua District and Huadu District is 60% and 69.3% respectively, which are defined as regions with low invasion risk; the distribution possibility in Baiyun District, Panyu District, Zengcheng District and Nansha District are much higher, which are identified as regions with high invasion risk; while the distribution possibility in Luogang District, Tianhe District and Huangpu District are the highest, which are determined as regions with highest risk.
期刊介绍:
Forestry Research is a comprehensive academic journal of forestry science organized by the Chinese Academy of Forestry. The main task is to reflect the latest research results, academic papers and research reports, scientific and technological developments and information on forestry science mainly organized by the Chinese Academy of Forestry, to promote academic exchanges at home and abroad, to carry out academic discussions, to flourish forestry science, and to better serve China's forestry construction.
The main contents are: forest seeds, seedling afforestation, forest plants, forest genetic breeding, tree physiology and biochemistry, forest insects, resource insects, forest pathology, forest microorganisms, forest birds and animals, forest soil, forest ecology, forest management, forest manager, forestry remote sensing, forestry biotechnology and other new technologies, new methods, and to increase the development strategy of forestry, the trend of development of disciplines, technology policies and strategies, etc., and to increase the forestry development strategy, the trend of development of disciplines, technology policies and strategies. It is suitable for scientists and technicians of forestry and related disciplines, teachers and students of colleges and universities, leaders and managers, and grassroots forestry workers.