has been cited by the following article(s):
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[1]
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Modeling PM2. 5 and PM10 Using a Robust Simplified Linear Regression Machine Learning Algorithm
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Caridade, PJSB Caridade - Atmosphere,
2022 |
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[2]
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PROFILING AND FORECASTING AIR POLLUTANT INDEX FOR MALAYSIA
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Thesis,
2017 |
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[3]
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Forecasting airborne pollutants via time-series models
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2017 |
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[4]
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Variable Selection Based on Statistical Learning Approaches to Improve PM10 Concentration Forecasting.
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Journal of Environmental Informatics,
2017 |
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[5]
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Analisys of taxi data for understanding urban dynamics
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2016 |
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[6]
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Analysis and prediction of PM10 concentration levels in Tunisia using statistical learning approaches
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Environmental and Ecological Statistics,
2016 |
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[7]
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Analysis and prediction of\ mathrm {PM} _ {10} concentration levels in Tunisia using statistical learning approaches
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Environmental and Ecological Statistics,
2016 |
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[8]
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Nearest Neighbour Based Forecast Model for PM10 Forecasting: Individual and Combination Forecasting
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Aerosol and Air Quality Research,
2015 |
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[9]
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Coherent Approach for Modeling and Nowcasting Hourly Near-Road Black Carbon Concentrations in Seattle, Washington
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Washington."Presented by Xiaoyue Liu at the 92nd Annual Meeting of Transportation Research Board, Washington DC,
2015 |
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[10]
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Analysis of daily average PM 10 predictions by generalized linear models in Brno, Czech Republic
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Atmospheric Pollution Research,
2014 |
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[11]
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Analysis of daily average PM10 predictions by generalized linear models in Brno, Czech Republic
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Atmospheric Pollution Research,
2014 |
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[12]
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PM10 forecasting using clusterwise regression
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Atmospheric Environment,
2011 |
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[1]
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Modeling PM2.5 and PM10 Using a Robust Simplified Linear Regression Machine Learning Algorithm
Atmosphere,
2022
DOI:10.3390/atmos13081334
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[2]
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Analysis and prediction of $$\mathrm{PM}_{10}$$ PM 10 concentration levels in Tunisia using statistical learning approaches
Environmental and Ecological Statistics,
2016
DOI:10.1007/s10651-016-0349-8
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[3]
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Coherent approach for modeling and nowcasting hourly near-road Black Carbon concentrations in Seattle, Washington
Transportation Research Part D: Transport and Environment,
2015
DOI:10.1016/j.trd.2014.10.009
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[4]
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Analysis of daily average PM10 predictions by generalized linear models in Brno, Czech Republic
Atmospheric Pollution Research,
2014
DOI:10.5094/APR.2014.055
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[5]
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PM10 forecasting using clusterwise regression
Atmospheric Environment,
2011
DOI:10.1016/j.atmosenv.2011.09.016
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