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 artificial neural network models for ambient air pollution prediction
Research activity in the field of air pollution forecasting using artificial neural networks
(ANNs) has increased dramatically in recent years. However, the development of ANN …
(ANNs) has increased dramatically in recent years. However, the development of ANN …
Multi-hour and multi-site air quality index forecasting in Bei**g using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering
R Yan, J Liao, J Yang, W Sun, M Nong, F Li - Expert Systems with …, 2021 - Elsevier
Effective air quality forecasting models are helpful for timely prevention and control of air
pollution. However, the spatiotemporal distribution characteristics of air quality have not …
pollution. However, the spatiotemporal distribution characteristics of air quality have not …
Forecasting air quality time series using deep learning
This paper presents one of the first applications of deep learning (DL) techniques to predict
air pollution time series. Air quality management relies extensively on time series data …
air pollution time series. Air quality management relies extensively on time series data …
PM10 and PM2. 5 real-time prediction models using an interpolated convolutional neural network
S Chae, J Shin, S Kwon, S Lee, S Kang, D Lee - Scientific Reports, 2021 - nature.com
In this paper, we propose a real-time prediction model that can respond to particulate
matters (PM) in the air, which are an indication of poor air quality. The model applies …
matters (PM) in the air, which are an indication of poor air quality. The model applies …
A hybrid model for PM2. 5 forecasting based on ensemble empirical mode decomposition and a general regression neural network
Exposure to high concentrations of fine particulate matter (PM 2.5) can cause serious health
problems because PM 2.5 contains microscopic solid or liquid droplets that are sufficiently …
problems because PM 2.5 contains microscopic solid or liquid droplets that are sufficiently …
Daily PM2. 5 concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm
W Sun, J Sun - Journal of environmental management, 2017 - Elsevier
Increased attention has been paid to PM 2.5 pollution in China. Due to its detrimental effects
on environment and health, it is important to establish a PM 2.5 concentration forecasting …
on environment and health, it is important to establish a PM 2.5 concentration forecasting …
Spatial assessment of air quality patterns in Malaysia using multivariate analysis
This study aims to investigate possible sources of air pollutants and the spatial patterns
within the eight selected Malaysian air monitoring stations based on a two-year database …
within the eight selected Malaysian air monitoring stations based on a two-year database …
Air quality early-warning system for cities in China
Y Xu, W Yang, J Wang - Atmospheric Environment, 2017 - Elsevier
Air pollution has become a serious issue in many develo** countries, especially in China,
and could generate adverse effects on human beings. Air quality early-warning systems play …
and could generate adverse effects on human beings. Air quality early-warning systems play …
PM2. 5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors
S Zhu, X Lian, L Wei, J Che, X Shen, L Yang… - Atmospheric …, 2018 - Elsevier
The PM 2.5 is the culprit of air pollution, and it leads to respiratory system disease when the
fine particles are inhaled. Therefore, it is increasingly significant to develop an effective …
fine particles are inhaled. Therefore, it is increasingly significant to develop an effective …