Methods used for handling and quantifying model uncertainty of artificial neural network models for air pollution forecasting
The use of data-driven techniques such as artificial neural network (ANN) models for
outdoor air pollution forecasting has been popular in the past two decades. However …
outdoor air pollution forecasting has been popular in the past two decades. However …
Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review
Urban air pollution is a pressing global issue driven by factors such as swift urbanization,
population expansion, and heightened industrial activities. To address this challenge, the …
population expansion, and heightened industrial activities. To address this challenge, the …
Potential of coupling metaheuristics-optimized-xgboost and shap in revealing pahs environmental fate
Polycyclic aromatic hydrocarbons (PAHs) refer to a group of several hundred compounds,
among which 16 are identified as priority pollutants, due to their adverse health effects …
among which 16 are identified as priority pollutants, due to their adverse health effects …
A novel hybrid prediction model for PM2.5 concentration based on decomposition ensemble and error correction
H Yang, J Zhao, G Li - Environmental Science and Pollution Research, 2023 - Springer
PM2. 5 concentration is an important index to measure the degree of air pollution. It is
necessary to establish an accurate PM2. 5 concentration prediction system for urban air …
necessary to establish an accurate PM2. 5 concentration prediction system for urban air …
Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain
This study introduces a forecasting model to help design an effective blood supply chain
mechanism for tackling the COVID-19 pandemic. In doing so, first, the number of people …
mechanism for tackling the COVID-19 pandemic. In doing so, first, the number of people …
Spatio-temporal visualization and forecasting of in the Brazilian state of Minas Gerais
KLS da Silva, JL López-Gonzales… - Scientific Reports, 2023 - nature.com
Air pollution due to air contamination by gases, liquids, and solid particles in suspension, is
a great environmental and public health concern nowadays. An important type of air …
a great environmental and public health concern nowadays. An important type of air …
An enhanced interval-valued PM2. 5 concentration forecasting model with attention-based feature extraction and self-adaptive combination technology
J Zhu, P Zheng, L Niu, H Chen, P Wu - Expert Systems with Applications, 2025 - Elsevier
Accurate predictions of air quality enable governments and relevant authorities to take
promptly measures for protecting public health. With the increasing time-varying nature of air …
promptly measures for protecting public health. With the increasing time-varying nature of air …
Ensemble of ensembles for fine particulate matter pollution prediction using big data analytics and IoT emission sensors
Purpose The study aims to develop a multilayer high-effective ensemble of ensembles
predictive model (stacking ensemble) using several hyperparameter optimized ensemble …
predictive model (stacking ensemble) using several hyperparameter optimized ensemble …
Daily PM2. 5 and PM10 forecasting using linear and nonlinear modeling framework based on robust local mean decomposition and moving window ensemble …
Z Wang, H Chen, J Zhu, Z Ding - Applied Soft Computing, 2022 - Elsevier
Highly accurate forecasting of particulate matter concentration (PMC) is essential and
effective for establishing a reliable air pollution early warning system and has both …
effective for establishing a reliable air pollution early warning system and has both …
[HTML][HTML] Air quality prediction based on singular spectrum analysis and artificial neural networks
Singular spectrum analysis is a powerful nonparametric technique used to decompose the
original time series into a set of components that can be interpreted as trend, seasonal, and …
original time series into a set of components that can be interpreted as trend, seasonal, and …