干旱区生态环境知识资源中心(Arid): Evaluating the ecological vulnerability of Chongqing using deep learning

Arid

DOI	10.1007/s11356-023-28032-8
	Evaluating the ecological vulnerability of Chongqing using deep learning
	Wu, Jun-Yi; Liu, Hong; Li, Tong; Ou-Yang, Yuan; Zhang, Jing-Hua; Zhang, Teng-Jiao; Huang, Yong; Gao, Wen-Long; Shao, Lu
通讯作者	Ou-Yang, Y
来源期刊	ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
ISSN	0944-1344
EISSN	1614-7499
出版年	2023
卷号	30 期号:36 页码:86365-86379
英文摘要	This study used deep learning to evaluate the ecological vulnerability of Chongqing, China, discuss the deep learning evaluations of ecological vulnerability, and generate vulnerability maps that support local ecological environment protection and governance decisions and provide reference for future studies. The information gain ratio was used to screen the influencing factors, selecting 16 factors that influence ecological vulnerability. Deep neural network (DNN) and convolutional neural network (CNN) methods were used for modeling, and two ecological vulnerability maps of the study area were generated. The results showed that the mean absolute error and root mean square error of the DNN and CNN models were relatively small, and the fitting accuracy was high. The area under the receiver operating characteristic curve of the CNN model was 0.926, which was better than that of the DNN model (0.888). Random forest was applied to calculate the importance of the influencing factors in the two models. Because the main factor was geological features, the relative ecological vulnerability was mainly affected by karst topography. Through the analysis of the ecological vulnerability map, the areas with higher vulnerability are the karst mountains of Dabashan, Wushan, and Qiyaoshan in the northeast and southeast, as well as the valley between mountains and cities in the center and west of the study area. According to the investigation of these areas, the primary ecological problems are low forest quality, structural irregularities caused by self-geological factors, severe desertification, and soil erosion. Human activity is also an important factor that causes ecological vulnerability in the study area. In conclusion, deep learning, particularly CNN models, can be used for ecological vulnerability assessments. The ecological vulnerability maps conformed to the basic cognition of field surveys and can provide references for other deep learning vulnerability studies. While the overall vulnerability of the study area is not high, ecological problems that lead to its vulnerability should be addressed by future ecological protection and management measures.
英文关键词	Chongqing China Convolutional neural network Deep neural network Ecosystem Evaluation map Karst mountain Random forest Three Gorges
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:001022591200001
WOS关键词	RANDOM FOREST ; CHINA ; ECOSYSTEM ; SENSITIVITY ; CLASSIFIER ; VEGETATION ; EROSION ; MODELS ; IMPACT ; REGION
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396282
推荐引用方式 GB/T 7714	Wu, Jun-Yi,Liu, Hong,Li, Tong,et al. Evaluating the ecological vulnerability of Chongqing using deep learning[J],2023,30(36):86365-86379.
APA	Wu, Jun-Yi.,Liu, Hong.,Li, Tong.,Ou-Yang, Yuan.,Zhang, Jing-Hua.,...&Shao, Lu.(2023).Evaluating the ecological vulnerability of Chongqing using deep learning.ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH,30(36),86365-86379.
MLA	Wu, Jun-Yi,et al."Evaluating the ecological vulnerability of Chongqing using deep learning".ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 30.36(2023):86365-86379.