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[HTML][HTML] Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies
H Taheri Shahraiyni, S Sodoudi - Atmosphere, 2016 - mdpi.com
PM10 prediction has attracted special legislative and scientific attention due to its harmful
effects on human health. Statistical techniques have the potential for high-accuracy PM10 …
effects on human health. Statistical techniques have the potential for high-accuracy PM10 …
[HTML][HTML] Haze prediction model using deep recurrent neural network
In recent years, haze pollution is frequent, which seriously affects daily life and production
process. The main factors to measure the degree of smoke pollution are the concentrations …
process. The main factors to measure the degree of smoke pollution are the concentrations …
Integrating low-cost sensor monitoring, satellite map**, and geospatial artificial intelligence for intra-urban air pollution predictions
There is a growing need to apply geospatial artificial intelligence analysis to disparate
environmental datasets to find solutions that benefit frontline communities. One such …
environmental datasets to find solutions that benefit frontline communities. One such …
PM2. 5 air pollution prediction through deep learning using meteorological, vehicular, and emission data: a case study of New Delhi, India
Abstract Particulate matter (PM 2.5) concentration is an air pollutant that can lead to serious
health complications in humans. The detection of this air pollutant is essential so that …
health complications in humans. The detection of this air pollutant is essential so that …
Assessing the accuracy of predictive models for numerical data: Not r nor r2, why not? Then what?
J Li - PloS one, 2017 - journals.plos.org
Assessing the accuracy of predictive models is critical because predictive models have been
increasingly used across various disciplines and predictive accuracy determines the quality …
increasingly used across various disciplines and predictive accuracy determines the quality …
A model for particulate matter (PM2. 5) prediction for Delhi based on machine learning approaches
Abstract Particulate matter (PM 2.5) remains one of the most dominant contributors to air
pollution in Delhi and its acute or chronic exposures have exerted serious health …
pollution in Delhi and its acute or chronic exposures have exerted serious health …
Can air temperature be used to project influences of climate change on stream temperature?
Worldwide, lack of data on stream temperature has motivated the use of regression-based
statistical models to predict stream temperatures based on more widely available data on air …
statistical models to predict stream temperatures based on more widely available data on air …
HazeEst: Machine learning based metropolitan air pollution estimation from fixed and mobile sensors
Metropolitan air pollution is a growing concern in both develo** and developed countries.
Fixed-station monitors, typically operated by governments, offer accurate but sparse data …
Fixed-station monitors, typically operated by governments, offer accurate but sparse data …
How neighborhood effect averaging might affect assessment of individual exposures to air pollution: A study of ozone exposures in Los Angeles
The neighborhood effect averaging problem (NEAP) can be a serious methodological
problem that leads to erroneous assessments when studying mobility-dependent exposures …
problem that leads to erroneous assessments when studying mobility-dependent exposures …
Assessment of sociodemographic disparities in environmental exposure might be erroneous due to neighborhood effect averaging: Implications for environmental …
The neighborhood effect averaging problem (NEAP) is a major methodological problem that
might affect the accuracy of assessments of individual exposure to mobility-dependent …
might affect the accuracy of assessments of individual exposure to mobility-dependent …