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Deep learning: To better understand how human activities affect the value of ecosystem services-A case study of Nanjing
Liu, Chang1; Qi, Yi2; Wang, Zhenbo3; Yu, Junlan1; Li, Shan1; Yao, Hong4; Ni, Tianhua1
2020-10-06
Source PublicationPLOS ONE
ISSN1932-6203
Volume15Issue:10Pages:16
Corresponding AuthorYao, Hong(yaohong@ntu.edu.cn) ; Ni, Tianhua(thni@nju.edu.cn)
AbstractThe 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.
DOI10.1371/journal.pone.0238789
WOS KeywordURBANIZATION ; BUNDLES ; IMPACTS ; AREAS ; CITY ; NEED
Indexed BySCI
Language英语
Funding ProjectDepartment of Ecology and Environment of Jiangsu Provvince[2018008] ; Department of Science and Technology of Jiangsu Province[201904]
Funding OrganizationDepartment of Ecology and Environment of Jiangsu Provvince ; Department of Science and Technology of Jiangsu Province
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000578473000046
PublisherPUBLIC LIBRARY SCIENCE
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/157170
Collection中国科学院地理科学与资源研究所
Corresponding AuthorYao, Hong; Ni, Tianhua
Affiliation1.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing, Peoples R China
2.Nanjing Univ, Sch Architecture & Urban Planning, Nanjing, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
4.Nantong Univ, Sch Geog, Nantong, Peoples R China
Recommended Citation
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.
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