摘要
基于BP人工神经网络模型,对黄河三角洲高效生态经济区土地生态系统脆弱性进行综合评价和时空演化分析,并借助灰色关联度模型探究其影响因素.结果表明:研究区土地生态系统脆弱性从2005年的1.244降低至2016年的1.113,脆弱性逐步改善;脆弱性由西到东、由内陆到沿海逐渐加剧,并表现出脆弱性平稳型和脆弱性渐低型2个演化特征;地均工业废水排放量、盐碱荒地面积比重、土地利用程度、建成区绿化覆盖率、节能环保支出占财政支出的比重是系统脆弱性的主要影响因素.因此,降低土地生态系统脆弱性的政策着力点应该集中在生态修复、优化土地利用结构和节能减排等方面.
The BP artificial neural network model was used to conduct comprehensive evaluations and spatio-temporal evolution analyses of land ecosystem vulnerability in the efficiency eco-economic zone of the Yellow River Delta, then the grey correlation degree model was applied to explore the influencing factors. The results showed that the land ecosystem vulnerability decreased to 1.113 in 2016 from 1.244 in 2005 in the study area, and improved gradually. The vulnerability gradually increased from the west to the east and from the inland to the coastal, which showed two evolutionary characteristics: steady vulnerability type and lower vulnerability type. Average industrial wastewater discharge, proportion of saline-alkali wasteland, the degree of land use, the green coverage of built-up areas, and the proportion of energy-saving and environmental protection expenditures to fiscal expenditures were the main influencing factors of systemic vulnerability. Therefore, the policy focus of reducing the vulnerability of land ecosystems should be on ecological restoration, land utilization structure optimization, energy conservation and emission reduction.
作者
张帅
董会忠
曾文霞
ZHANG Shuai;DONG Hui-zhong;ZENG Wen-xia(School of Management, Shandong University of Technology, Zibo 255012, China)
出处
《中国环境科学》
EI
CAS
CSCD
北大核心
2019年第4期1696-1704,共9页
China Environmental Science
基金
山东省重大理论与实践问题研究专项(18BSJJ05)
关键词
土地生态系统
脆弱性
BP人工神经网络
时空演化
land ecosystem
vulnerability
BP artificial neural network
spatiotemporal evolution
