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 systematic literature review of deep learning neural network for time series air quality forecasting
Rapid progress of industrial development, urbanization and traffic has caused air quality
reduction that negatively affects human health and environmental sustainability, especially …
reduction that negatively affects human health and environmental sustainability, especially …
Monthly global estimates of fine particulate matter and their uncertainty
A Van Donkelaar, MS Hammer, L Bindle… - Environmental …, 2021 - ACS Publications
Annual global satellite-based estimates of fine particulate matter (PM2. 5) are widely relied
upon for air-quality assessment. Here, we develop and apply a methodology for monthly …
upon for air-quality assessment. Here, we develop and apply a methodology for monthly …
Machine learning: new ideas and tools in environmental science and engineering
The rapid increase in both the quantity and complexity of data that are being generated daily
in the field of environmental science and engineering (ESE) demands accompanied …
in the field of environmental science and engineering (ESE) demands accompanied …
Data-driven machine learning in environmental pollution: gains and problems
The complexity and dynamics of the environment make it extremely difficult to directly predict
and trace the temporal and spatial changes in pollution. In the past decade, the …
and trace the temporal and spatial changes in pollution. In the past decade, the …
Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US
Smoke from wildfires is a growing health risk across the US. Understanding the spatial and
temporal patterns of such exposure and its population health impacts requires separating …
temporal patterns of such exposure and its population health impacts requires separating …
[HTML][HTML] An ensemble-based model of PM2. 5 concentration across the contiguous United States with high spatiotemporal resolution
Various approaches have been proposed to model PM 2.5 in the recent decade, with
satellite-derived aerosol optical depth, land-use variables, chemical transport model …
satellite-derived aerosol optical depth, land-use variables, chemical transport model …
Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees
Fine particulate matter with aerodynamic diameters≤ 2.5 µ m (PM 2.5) has adverse effects
on human health and the atmospheric environment. The estimation of surface PM 2.5 …
on human health and the atmospheric environment. The estimation of surface PM 2.5 …
Estimating 1-km-resolution PM2. 5 concentrations across China using the space-time random forest approach
Abstract Fine particulate matter (PM 2.5) is closely related to the atmospheric environment
and human life. Satellite-based aerosol optical depth (AOD) products have been widely …
and human life. Satellite-based aerosol optical depth (AOD) products have been widely …
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 …