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 …
A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science
Air pollution is a global geo-hazard with significant implications, including deterioration of
health and premature death. Climatic variables such as temperature, rainfall, wind, and …
health and premature death. Climatic variables such as temperature, rainfall, wind, and …
[HTML][HTML] An LSTM-based aggregated model for air pollution forecasting
During the past few years, severe air-pollution problem has garnered worldwide attention
due to its effect on health and wellbeing of individuals. As a result, the analysis and …
due to its effect on health and wellbeing of individuals. As a result, the analysis and …
Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting
L Wen, K Zhou, S Yang, X Lu - Energy, 2019 - Elsevier
A deep recurrent neural network with long short-term memory units (DRNN-LSTM) model is
developed to forecast aggregated power load and the photovoltaic (PV) power output in …
developed to forecast aggregated power load and the photovoltaic (PV) power output in …
National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?
As the volatility of electricity demand increases owing to climate change and electrification,
the importance of accurate peak load forecasting is increasing. Traditional peak load …
the importance of accurate peak load forecasting is increasing. Traditional peak load …
[HTML][HTML] Air pollution prediction using long short-term memory (LSTM) and deep autoencoder (DAE) models
T Xayasouk, HM Lee, G Lee - Sustainability, 2020 - mdpi.com
Many countries worldwide have poor air quality due to the emission of particulate matter (ie,
PM10 and PM2. 5), which has led to concerns about human health impacts in urban areas …
PM10 and PM2. 5), which has led to concerns about human health impacts in urban areas …
Air pollution forecasting using RNN with LSTM
YT Tsai, YR Zeng, YS Chang - 2018 IEEE 16th Intl Conf on …, 2018 - ieeexplore.ieee.org
With the advance of technology, it is increasingly exhaust emissions have caused air
pollution. In particular, PM2. 5 (Particulate Matter) has been proven that it has a great …
pollution. In particular, PM2. 5 (Particulate Matter) has been proven that it has a great …
Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters
J Kleine Deters, R Zalakeviciute… - Journal of Electrical …, 2017 - Wiley Online Library
Outdoor air pollution costs millions of premature deaths annually, mostly due to
anthropogenic fine particulate matter (or PM2. 5). Quito, the capital city of Ecuador, is no …
anthropogenic fine particulate matter (or PM2. 5). Quito, the capital city of Ecuador, is no …
Relevance analysis and short-term prediction of PM2. 5 concentrations in Beijing based on multi-source data
XY Ni, H Huang, WP Du - Atmospheric environment, 2017 - Elsevier
The PM 2.5 problem is proving to be a major public crisis and is of great public-concern
requiring an urgent response. Information about, and prediction of PM 2.5 from the …
requiring an urgent response. Information about, and prediction of PM 2.5 from the …
Applying machine learning methods in managing urban concentrations of traffic-related particulate matter (PM10 and PM2. 5)
A Suleiman, MR Tight, AD Quinn - Atmospheric Pollution Research, 2019 - Elsevier
This study presents a new method for evaluating the effectiveness of roadside PM 10 and
PM 2.5 reduction scenarios using Machine Learning (ML) based models. The ML methods …
PM 2.5 reduction scenarios using Machine Learning (ML) based models. The ML methods …