[HTML][HTML] RNN-LSTM: From applications to modeling techniques and beyond—Systematic review

SM Al-Selwi, MF Hassan, SJ Abdulkadir… - Journal of King Saud …, 2024 - Elsevier
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 …

[HTML][HTML] Air quality prediction in smart cities using machine learning technologies based on sensor data: a review

D Iskandaryan, F Ramos, S Trilles - Applied Sciences, 2020 - mdpi.com
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 …

[HTML][HTML] An LSTM-based aggregated model for air pollution forecasting

YS Chang, HT Chiao, S Abimannan, YP Huang… - Atmospheric Pollution …, 2020 - Elsevier
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 …

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 …

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) …

Application of complete ensemble empirical mode decomposition based multi-stream informer (CEEMD-MsI) in PM2. 5 concentration long-term prediction

Q Zheng, X Tian, Z Yu, B **, N Jiang, Y Ding… - Expert Systems with …, 2024 - Elsevier
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 …

Air quality prediction using CNN+ LSTM-based hybrid deep learning architecture

A Gilik, AS Ogrenci, A Ozmen - Environmental science and pollution …, 2022 - Springer
Air pollution prediction based on variables in environmental monitoring data gains further
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

J Ma, JCP Cheng, F Jiang, W Chen, M Wang… - Energy and Buildings, 2020 - Elsevier
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 …

Air quality prediction at new stations using spatially transferred bi-directional long short-term memory network

J Ma, Z Li, JCP Cheng, Y Ding, C Lin, Z Xu - Science of The Total …, 2020 - Elsevier
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 …

Machine learning algorithms to forecast air quality: a survey

M Méndez, MG Merayo, M Núñez - Artificial Intelligence Review, 2023 - Springer
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 …