ArticleEnvironment international2020
Ensemble-based deep learning for estimating PM
Article in Environment international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Machine Learning Predictions of PMGeoHealth · 2026Article
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- Fusing satellite imagery and ground-based observations for PMScientific reports · 2025Article
- Adverse Birth Outcomes Associated with Heat Stress and Wildfire Smoke Exposure During Preconception and Pregnancy.Environmental science & technology · 2025Article
- A hybrid deep learning air pollution prediction approach based on neighborhood selection and spatio-temporal attention.Scientific reports · 2025Article
- Multiresolution Analysis of HRRR Meteorological Parameters and GOES-R AOD for Hourly PMEnvironmental science & technology · 2024Article
- Deep-learning architecture for PMEnvironmental science and ecotechnology · 2024Review
- Mortality attributable to PMScience advances · 2024Article
- Exposure to particulate matter and ozone, locations of regulatory monitors, and sociodemographic disparities in the city of Rio de Janeiro: Based on local air pollution estimates generated from machine learning modelsAtmospheric environment (Oxford, England : 1994) · 2024Article
- Prediction of Pollutant Concentration Based on Spatial-Temporal Attention, ResNet and ConvLSTM.Sensors (Basel, Switzerland) · 2023Article
- Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment.Journal of exposure science & environmental epidemiology · 2023Article
- Influence of land-sea breeze on PM[Formula: see text] prediction in central and southern Taiwan using composite neural network.Scientific reports · 2023Article
- A novel ensemble-based statistical approach to estimate daily wildfire-specific PMEnvironment international · 2023Article
- Application of geostationary satellite and high-resolution meteorology data in estimating hourly PMRemote sensing of environment · 2022Article
- Wildfires in Pregnancy: Potential Threats to the Newborn.Paediatric and perinatal epidemiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
introductionEstimating PM
methodsUsing ensemble-based deep learning with big data fused from multiple sources we developed a PM
resultsEnsemble deep learning to predict PM
conclusionOur method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.