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 …
A review of machine learning applications in wildfire science and management
Artificial intelligence has been applied in wildfire science and management since the 1990s,
with early applications including neural networks and expert systems. Since then, the field …
with early applications including neural networks and expert systems. Since then, the field …
Global biogeography and projection of soil antibiotic resistance genes
Although edaphic antibiotic resistance genes (ARGs) pose serious threats to human well-
being, their spatially explicit patterns and responses to environmental constraints at the …
being, their spatially explicit patterns and responses to environmental constraints at the …
A machine learning method to estimate PM2. 5 concentrations across China with remote sensing, meteorological and land use information
Background Machine learning algorithms have very high predictive ability. However, no
study has used machine learning to estimate historical concentrations of PM 2.5 (particulate …
study has used machine learning to estimate historical concentrations of PM 2.5 (particulate …
Wildfire and prescribed burning impacts on air quality in the United States
Air quality impacts from wildfires have been dramatic in recent years, with millions of people
exposed to elevated and sometimes hazardous fine particulate matter (PM 2.5) …
exposed to elevated and sometimes hazardous fine particulate matter (PM 2.5) …
Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach
To estimate PM2. 5 concentrations, many parametric regression models have been
developed, while nonparametric machine learning algorithms are used less often and …
developed, while nonparametric machine learning algorithms are used less often and …
Spatiotemporal analysis of haze in Beijing based on the multi-convolution model
As a kind of air pollution, haze has complex temporal and spatial characteristics. From the
perspective of time, haze has different causes and levels of pollution in different seasons …
perspective of time, haze has different causes and levels of pollution in different seasons …
Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques
River flooding can be a highly destructive natural hazard. Numerous approaches have been
used to study the phenomenon; however, insufficient knowledge regarding flood …
used to study the phenomenon; however, insufficient knowledge regarding flood …
An Ensemble Machine-Learning Model To Predict Historical PM2.5 Concentrations in China from Satellite Data
The long satellite aerosol data record enables assessments of historical PM2. 5 level in
regions where routine PM2. 5 monitoring began only recently. However, most previous …
regions where routine PM2. 5 monitoring began only recently. However, most previous …
[HTML][HTML] A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen …
J Chen, K de Hoogh, J Gulliver, B Hoffmann… - Environment …, 2019 - Elsevier
Empirical spatial air pollution models have been applied extensively to assess exposure in
epidemiological studies with increasingly sophisticated and complex statistical algorithms …
epidemiological studies with increasingly sophisticated and complex statistical algorithms …