Machine learning in environmental research: common pitfalls and best practices
Machine learning (ML) is increasingly used in environmental research to process large data
sets and decipher complex relationships between system variables. However, due to the …
sets and decipher complex relationships between system variables. However, due to the …
Tackling environmental challenges in pollution controls using artificial intelligence: A review
This review presents the developments in artificial intelligence technologies for
environmental pollution controls. A number of AI approaches, which start with the reliable …
environmental pollution controls. A number of AI approaches, which start with the reliable …
Deep learning-based PM2. 5 prediction considering the spatiotemporal correlations: A case study of Beijing, China
U Pak, J Ma, U Ryu, K Ryom, U Juhyok, K Pak… - Science of the Total …, 2020 - Elsevier
Air pollution is one of the serious environmental problems that humankind faces and also a
hot topic in Northeastern Asia. Therefore, the accurate prediction of PM2. 5 (particulate …
hot topic in Northeastern Asia. Therefore, the accurate prediction of PM2. 5 (particulate …
A haze prediction method based on one-dimensional convolutional neural network
In recent years, more and more people are paying close attention to the environmental
problems in metropolitan areas and their harm to the human body. Among them, haze is the …
problems in metropolitan areas and their harm to the human body. Among them, haze is the …
An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration
W Qiao, Y Wang, J Zhang, W Tian, Y Tian… - Journal of Environmental …, 2021 - Elsevier
Wavelet transform (WT) is an advanced preprocessing technique, which has been widely
used in PM 10 prediction. However, this technique cannot provide stable performance due …
used in PM 10 prediction. However, this technique cannot provide stable performance due …
Forecasting air quality time series using deep learning
This paper presents one of the first applications of deep learning (DL) techniques to predict
air pollution time series. Air quality management relies extensively on time series data …
air pollution time series. Air quality management relies extensively on time series data …
Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts
Timely regional air quality forecasting in a city is crucial and beneficial for supporting
environmental management decisions as well as averting serious accidents caused by air …
environmental management decisions as well as averting serious accidents caused by air …
Modeling air quality prediction using a deep learning approach: Method optimization and evaluation
W Mao, W Wang, L Jiao, S Zhao, A Liu - Sustainable Cities and Society, 2021 - Elsevier
Air pollution is one of the hot issues that attracted widespread attention from urban and
society management. Air quality prediction is to issue an alarm when severe pollution …
society management. Air quality prediction is to issue an alarm when severe pollution …
Recursive neural network model for analysis and forecast of PM10 and PM2. 5
F Biancofiore, M Busilacchio, M Verdecchia… - Atmospheric Pollution …, 2017 - Elsevier
Atmospheric particulate matter (PM) is one of the pollutant that may have a significant impact
on human health. Data collected during three years in an urban area of the Adriatic coast …
on human health. Data collected during three years in an urban area of the Adriatic coast …
Spatial estimation of urban air pollution with the use of artificial neural network models
A Alimissis, K Philippopoulos, CG Tzanis… - Atmospheric …, 2018 - Elsevier
The deterioration of urban air quality is considered worldwide one of the primary
environmental issues and scientific evidence associates the exposure to ambient air …
environmental issues and scientific evidence associates the exposure to ambient air …