Deep learning: To better understand how human activities affect the value of ecosystem services-A case study of Nanjing

doi:10.1371/journal.pone.0238789

	Deep learning: To better understand how human activities affect the value of ecosystem services-A case study of Nanjing
	Liu, Chang 2; Qi, Yi 3; Wang, Zhenbo 4; Yu, Junlan 2; Li, Shan 2; Yao, Hong 1; Ni, Tianhua 2
刊名	PLOS ONE
	2020-10-06
卷号	15 期号:10 页码:16
ISSN号	1932-6203
DOI	10.1371/journal.pone.0238789
通讯作者	Yao, Hong(yaohong@ntu.edu.cn) ; Ni, Tianhua(thni@nju.edu.cn)
英文摘要	The value of ecosystem services is affected by increasing human activities. However, the anthropogenic driving mechanisms of ecosystem services are poorly understood. Here, we established a deep learning model to approximate the ecosystem service value (ESV) of Nanjing City using 23 socioeconomic factors. A multi-view analysis was then conducted on feasible impact mechanisms using model disassembly. The results indicated that certain factors had their own significant and independent effects on ESV, such as the proportion of water areas in the land-use structure and the output value of the secondary industry. The proportion of ecological water should be increased as much as possible, whereas the output value of the secondary industry should be reasonably controlled in Nanjing. Other intrinsically related factors were likely to be composited together to affect ESV, such as industrial water consumption and industrial electricity consumption. In Nanjing, simultaneously optimizing socio-economic factors related to city size, resources, and energy use efficiency likely represents an effective management strategy for maintaining and enhancing regional ecological service capabilities. The results of this work suggest that deep learning is an effective method of deepening studies on the prediction of ESV trends and human-driven mechanisms.
资助项目	Department of Ecology and Environment of Jiangsu Provvince[2018008] ; Department of Science and Technology of Jiangsu Province[201904]
WOS关键词	URBANIZATION ; BUNDLES ; IMPACTS ; AREAS ; CITY ; NEED
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	PUBLIC LIBRARY SCIENCE
WOS记录号	WOS:000578473000046
资助机构	Department of Ecology and Environment of Jiangsu Provvince ; Department of Science and Technology of Jiangsu Province
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/157169]
专题	中国科学院地理科学与资源研究所
通讯作者	Yao, Hong; Ni, Tianhua
作者单位	1.Nantong Univ, Sch Geog, Nantong, Peoples R China 2.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing, Peoples R China 3.Nanjing Univ, Sch Architecture & Urban Planning, Nanjing, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Liu, Chang,Qi, Yi,Wang, Zhenbo,et al. Deep learning: To better understand how human activities affect the value of ecosystem services-A case study of Nanjing[J]. PLOS ONE,2020,15(10):16.
APA	Liu, Chang.,Qi, Yi.,Wang, Zhenbo.,Yu, Junlan.,Li, Shan.,...&Ni, Tianhua.(2020).Deep learning: To better understand how human activities affect the value of ecosystem services-A case study of Nanjing.PLOS ONE,15(10),16.
MLA	Liu, Chang,et al."Deep learning: To better understand how human activities affect the value of ecosystem services-A case study of Nanjing".PLOS ONE 15.10(2020):16.