Data Science in Air Quality Monitoring
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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 environment...
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.
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