摘要
按照"美丽中国"建设的时代内涵要求,从生态、经济、政治、文化、社会等5个维度入手,尝试性地构建包含32个单项指标的"美丽湖南"建设水平定量综合评价指标体系及评判标准;以市(州)为评价单元,探索性地采用BP人工神经网络方法建模评价"美丽湖南"建设现状。结果表明:1)分维度看,2011年湖南省各市(州)政治子系统评价均值为0.651 4,评价结果为Ⅱ级,处于"美丽"状态;生态、经济和社会子系统评价均值分别为0.501 8、0.450 2和0.473 0,评价结果为Ⅲ级,处于"较美丽"状态;文化子系统评价均值为0.419 7,评价结果为Ⅳ级,处于"欠美丽"状态。2)整体而言,2011年湖南省各市(州)系统综合评价均值为0.492 7,综合评价结果为Ⅲ级,总体上湖南省处于"较美丽"状态。3)运用BP人工神经网络建模评价,简便高效,结果客观可靠,在区域建设水平综合评价中适用、有效。
According to the times connotation requirement of Beautiful China construction, this paper built a set of evaluation index system including 32 indexes and its judgment standard of Beautiful Hunan construction from ecological subsystem, economic subsystem, political subsystem, cultural subsystem and social subsystem, made a quantitative and comprehensive evaluation of Beautiful Hunan construction by BP artificial neural network model at city level. The results are as follows： 1） In 2011, the mean evaluation value of political subsystem was 0.651 4, which was of grade Ⅱ, that means the political subsystem in Hunan Province was in “beautiful” status; the mean evaluation values of ecological, economic and social subsystems were 0.501 8, 0.450 2, and 0.473 0, respectively, the results were at grade Ⅲ, that means those subsystems were in “less beautiful” status; the mean evaluation value of cultural subsystem was 0.419 7, which was of grade Ⅳ, that means the cultural subsystem was in “the least beautiful” status. 2） As a whole, the mean value of the system comprehensive evaluation was 0.492 7, the evaluation result was of grade Ⅲ, showing that Hunan Province was in “less beautiful” status in year 2011; 3） The BP artificial neural network method is simple and highly effective, its evaluation result is objective and reliable. The method would be applicable and valid in comprehensive evaluation of regional construction level.
出处
《热带地理》
北大核心
2014年第4期553-560,共8页
Tropical Geography
基金
教育部人文社会科学重点研究基地重大项目(14JJD720016)
晓庄学院生态文明研究院开放基金项目(2013)
晓庄学院青年优秀人才培养计划(ET13106)
关键词
“美丽湖南”
“美丽中国”
定量综合评价
BP
人工神经网络
Beautiful Hunan
Beautiful China
quantitative＆amp
comprehensive evaluation
BP artificial neural network
