Soft sensor for predicting indoor PM<sub>2.5</sub> concentration in subway with adaptive boosting deep learning model.

Wang, Jinyong; Wang, Dongsheng; Zhang, Fengshan; Yoo, ChangKyoo; Liu, Hongbin

Soft sensor for predicting indoor PM_2.5 concentration in subway with adaptive boosting deep learning model.

Wang, Jinyong; Wang, Dongsheng; Zhang, Fengshan; Yoo, ChangKyoo; Liu, Hongbin.

Afiliação

Wang J; Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China.
Wang D; College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Zhang F; Laboratory for Comprehensive Utilization of Paper Waste of Shandong Province, Shandong Huatai Paper Co. Ltd., Dongying 257335, China.
Yoo C; Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University, Yongin 446701, the Republic of Korea.
Liu H; Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; Guangxi Key Laboratory of Clean Pulp & Papermaking and Pollution Control, College of Light Industry and Food Engineering, Guangxi University, Nanning 5300

J Hazard Mater ; 465: 133074, 2024 Mar 05.

Article em En | MEDLINE | ID: mdl-38029591

Palavras-chave

Adaptive boosting; Ensemble learning; Indoor air quality; Long short-term memory; Soft sensor

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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo

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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China