This book presents a series of state-of-the-art methods for air quality monitoring in various engineering environment by using data science. In the book, the data-driven key techniques of the preprocessing, decomposition, identification, clustering, forecasting and interpolation of the air quality monitoring are explained in details with lots of experimental simulation. The book can provide important reference for the development of data science technologies in engineering air quality monitoring. The book can be used for students, engineers, scientists and managers in the fields of environmental engineering, atmospheric science, urban climate, civil engineering, traffic and vehicle engineering, etc.
"synopsis" may belong to another edition of this title.
Dr. Hui Liu is a Full Professor in the field of Artificial Intelligence, Smart Cities and Smart Energy at Central South University (CSU), China. Prof. Liu is the director of Institute of Artificial Intelligence and Robotics at CSU. He received double Ph.D degrees from Central South University (China) in 2011 and University of Rostock (Germany) in 2013, respectively. He received habilitation degree from University of Rostock in 2016. He was appointed as the BMBF junior group leader by the Ministry of Education and Research of Germany at University of Rostock since January, 2015 until December 2016.
Dr. Yanfei Li is an Associate Professor in the field of Artificial Intelligence, Smart Agriculture and Smart Energy at Hunan Agricultural University (HAU), China. Prof. Li is the director of Institute of Artificial Intelligence at HAU. She received Ph.D degree from University of Rostock (Germany) in 2014 then worked as a postdoctoral fellow at University of Rostock in 2015.
Dr. Zhu Duan is an assistant researcher in the field of Data Science at Central South University. Dr. Duan focuses on the research of time series prediction. He was selected as the Postdoctoral Fellowship Program of CPSF and Xiaohe Science and Technology Talent of Hunan Province in China.
This book presents a series of state-of-the-art methods for air quality monitoring in various engineering environment by using data science. In the book, the data-driven key techniques of the preprocessing, decomposition, identification, clustering, forecasting and interpolation of the air quality monitoring are explained in details with lots of experimental simulation. The book can provide important reference for the development of data science technologies in engineering air quality monitoring. The book can be used for students, engineers, scientists and managers in the fields of environmental engineering, atmospheric science, urban climate, civil engineering, traffic and vehicle engineering, etc.
"About this title" may belong to another edition of this title.
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware Englisch. Seller Inventory # 9789819657797
Seller: moluna, Greven, Germany
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Seller Inventory # 3502411369
Quantity: Over 20 available
Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: Neu. Data Science in Air Quality Monitoring | Hui Liu (u. a.) | Taschenbuch | Engineering Applications of Computational Methods | xxii | Englisch | 2026 | Springer | EAN 9789819657797 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 135828452
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg Englisch. Seller Inventory # 9789819657797
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a series of state-of-the-art methods for air quality monitoring in various engineering environment by using data science. In the book, the data-driven key techniques of the preprocessing, decomposition, identification, clustering, forecasting and interpolation of the air quality monitoring are explained in details with lots of experimental simulation. The book can provide important reference for the development of data science technologies in engineering air quality monitoring. The book can be used for students, engineers, scientists and managers in the fields of environmental engineering, atmospheric science, urban climate, civil engineering, traffic and vehicle engineering, etc. Seller Inventory # 9789819657797