干旱区生态环境知识资源中心(Arid): Green roofs and their effect on architectural design and urban ecology using deep learning approaches

Arid

DOI	10.1007/s00500-024-09637-8
	Green roofs and their effect on architectural design and urban ecology using deep learning approaches
	Wang, Chongyu; Guo, Jiayin; Liu, Juan
通讯作者	Wang, CY
来源期刊	SOFT COMPUTING
ISSN	1432-7643
EISSN	1433-7479
出版年	2024
卷号	28 期号:4 页码:3667-3682
英文摘要	In recent years, the rapid development of the world's economy has led to the large-scale development and utilization of ecological resources on the earth, due to which the ecological environment has been continuously and seriously damaged, resulting in the waste of resources, soil erosion, land desertification, etc. To avoid further damage to the ecological environment and ecological resources, improve the utilization rate of ecological resources, and ensure the sustainable development of human society, it is necessary to evaluate the ecological environment. In this study, we collected the required data using the Delphi method and remote sensing technology. Secondly, the green Olympic building evaluation system (which refers to the CASBEE method in Japan) was used to evaluate the impact of green roofs on architectural design and the urban ecological environment. Third, a deep learning (DL)-based hybrid model, which consists of a convolutional neural network (CNN) and long-short-term memory (SLSTM), known as CNN-LSTM, was used to evaluate the impact of green roofs on urban ecology and building architectural design. The influence of thermal comfort on the indoor environment of green roof buildings was studied. For experimentation, six samples of Shanghai Thumb Plaza, Splendid Tesco Point, Chaoshan Yuan Hotel, Green Management Office, Huangpu District Domestic Waste Transfer Station, and Changning District Fuxin Slaughterhouse were selected as evaluation objects, and the effect of green roofs on building design and urban ecology was evaluated from six levels: ecological, ornamental, safety, functional, social, and economic. Both the CASBEE and DL-based methods, CNN-LSTM, performed well and increased the evaluation results to some extent. The CNN-LSTM model increased the accuracy of the system by 3.55%, precision by 3.50%, recall by 4.46%, and F1-score by 3.30%. Overall, this study summarizes the existing problems of green rooftop buildings in Shanghai at this stage, which is conducive to formulating optimization strategies to improve the ecological benefits of green roof buildings and has important practical significance for realizing the sustainable development of human society.
英文关键词	Roof greening Architectural design Urban ecological environment Impact of green roofs Deep learning CNN-LSTM model
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001149213800006
WOS关键词	PERFORMANCE ; IMPACT
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405636
推荐引用方式 GB/T 7714	Wang, Chongyu,Guo, Jiayin,Liu, Juan. Green roofs and their effect on architectural design and urban ecology using deep learning approaches[J],2024,28(4):3667-3682.
APA	Wang, Chongyu,Guo, Jiayin,&Liu, Juan.(2024).Green roofs and their effect on architectural design and urban ecology using deep learning approaches.SOFT COMPUTING,28(4),3667-3682.
MLA	Wang, Chongyu,et al."Green roofs and their effect on architectural design and urban ecology using deep learning approaches".SOFT COMPUTING 28.4(2024):3667-3682.