Evaluating the ecological vulnerability of Chongqing using deep learning-Reference-Cited by-同舟云学术

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Evaluating the ecological vulnerability of Chongqing using deep learning

Published:2023-07-05 Issue:36 Volume:30 Page:86365-86379
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Wu Jun-Yi,Liu Hong,Li Tong,Ou-Yang Yuan^ORCID,Zhang Jing-Hua,Zhang Teng-Jiao,Huang Yong,Gao Wen-Long,Shao Lu

Funder

National Natural Science Foundation of China

International Geosciences Programme

China Geological Survey Project

Liu Baojun Academician Fund

International Scientific Plan of the Qinghai-Xizang Plateau of Southwest Geological Science

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11356-023-28032-8.pdf

Reference63 articles.

1. Abdullah S, Barua D (2022) Combining Geographical Information System (GIS) and deep learning to monitor and predict vegetation vulnerability: an Empirical Study on Nijhum Dwip, Bangladesh. Ecol Eng 178. https://doi.org/10.1016/j.ecoleng.2022.106577

2. Berrouet L, Villegas-Palacio C, Botero V (2019) A social vulnerability index to changes in ecosystem services provision at local scale: a methodological approach. Environ Sci Policy 93:158–171. https://doi.org/10.1016/j.envsci.2018.12.011

3. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

4. Chandra A, Gaganis P (2016) Deconstructing vulnerability and adaptation in a coastal river basin ecosystem: a participatory analysis of flood risk in Nadi, Fiji Islands. Clim Dev 8:256–269. https://doi.org/10.1080/17565529.2015.1016884

5. Chen WX (2022) A multi-scale assessment of ecosystem health based on the pressure-state-response framework: a case in the middle reaches of the Yangtze River Urban Agglomerations, China. Environ Sci Pollut Res 29:29202–29219. https://doi.org/10.1007/s11356-021-17768

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