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[HTML][HTML] RNN-LSTM: From applications to modeling techniques and beyond—Systematic review
Abstract Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN)
algorithm known for its ability to effectively analyze and process sequential data with long …
algorithm known for its ability to effectively analyze and process sequential data with long …
[HTML][HTML] Air quality prediction in smart cities using machine learning technologies based on sensor data: a review
The influence of machine learning technologies is rapidly increasing and penetrating almost
in every field, and air pollution prediction is not being excluded from those fields. This paper …
in every field, and air pollution prediction is not being excluded from those fields. This paper …
[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 …
PM2. 5 concentration forecasting at surface monitoring sites using GRU neural network based on empirical mode decomposition
G Huang, X Li, B Zhang, J Ren - Science of the Total Environment, 2021 - Elsevier
The main component of haze is the particulate matter (PM) 2.5. How to explore the laws of
PM2. 5 concentration changes is the main content of air quality prediction. Combining the …
PM2. 5 concentration changes is the main content of air quality prediction. Combining the …
Status of air pollution during COVID-19-induced lockdown in Delhi, India
H Singh, G Meraj, S Singh, V Shrivastava, V Sharma… - Atmosphere, 2022 - mdpi.com
To monitor the spread of the novel coronavirus (COVID-19), India, during the last week of
March 2020, imposed national restrictions on the movement of its citizens (lockdown) …
March 2020, imposed national restrictions on the movement of its citizens (lockdown) …
Application of complete ensemble empirical mode decomposition based multi-stream informer (CEEMD-MsI) in PM2. 5 concentration long-term prediction
Nowadays, air pollution has become one of the most serious environmental problems facing
humanity and an inescapable obstacle limiting the sustainable development of cities and …
humanity and an inescapable obstacle limiting the sustainable development of cities and …
Air quality prediction using CNN+ LSTM-based hybrid deep learning architecture
Air pollution prediction based on variables in environmental monitoring data gains further
importance with increasing concerns about climate change and the sustainability of cities …
importance with increasing concerns about climate change and the sustainability of cities …
A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data
Improving the energy efficiency of the buildings is a worldwide hot topic nowadays. To assist
comprehensive analysis and smart management, high-quality historical data records of the …
comprehensive analysis and smart management, high-quality historical data records of the …
Air quality prediction at new stations using spatially transferred bi-directional long short-term memory network
In the last decades, air pollution has been a critical environmental issue, especially in
develo** countries like China. The governments and scholars have spent lots of effort on …
develo** countries like China. The governments and scholars have spent lots of effort on …
Machine learning algorithms to forecast air quality: a survey
Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is
important to develop forecasting mechanisms that can be used by the authorities, so that …
important to develop forecasting mechanisms that can be used by the authorities, so that …