A review on the long short-term memory model
Long short-term memory (LSTM) has transformed both machine learning and
neurocomputing fields. According to several online sources, this model has improved …
neurocomputing fields. According to several online sources, this model has improved …
Deep learning for air pollutant concentration prediction: A review
Air pollution has become one of the critical environmental problem in the 21st century and
has attracted worldwide attentions. To mitigate it, many researchers have investigated the …
has attracted worldwide attentions. To mitigate it, many researchers have investigated the …
Air-pollution prediction in smart city, deep learning approach
Over the past few decades, due to human activities, industrialization, and urbanization, air
pollution has become a life-threatening factor in many countries around the world. Among …
pollution has become a life-threatening factor in many countries around the world. Among …
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 …
A hybrid CNN-LSTM model for forecasting particulate matter (PM2. 5)
T Li, M Hua, XU Wu - Ieee Access, 2020 - ieeexplore.ieee.org
PM2. 5 is one of the most important pollutants related to air quality, and the increase of its
concentration will aggravate the threat to people's health. Therefore, the prediction of …
concentration will aggravate the threat to people's health. Therefore, the prediction of …
[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 …
Prediction of instantaneous yield of bio-oil in fluidized biomass pyrolysis using long short-term memory network based on computational fluid dynamics data
Computational fluid dynamics (CFD) is an effective tool to investigate biomass fast pyrolysis
in fluidized bed reactor for bio-oil production, while it requires huge computational time …
in fluidized bed reactor for bio-oil production, while it requires huge computational time …
Application of wavelet-packet transform driven deep learning method in PM2. 5 concentration prediction: A case study of Qingdao, China
Air pollution is one of the most serious environmental problems faced by human beings, and
it is also a hot topic in the development of sustainable cities. Accurate PM 2.5 prediction …
it is also a hot topic in the development of sustainable cities. Accurate PM 2.5 prediction …
A variational Bayesian deep network with data self-screening layer for massive time-series data forecasting
Compared with mechanism-based modeling methods, data-driven modeling based on big
data has become a popular research field in recent years because of its applicability …
data has become a popular research field in recent years because of its applicability …
Transformers for modeling physical systems
Transformers are widely used in natural language processing due to their ability to model
longer-term dependencies in text. Although these models achieve state-of-the-art …
longer-term dependencies in text. Although these models achieve state-of-the-art …