[HTML][HTML] A deep belief network combined with modified grey wolf optimization algorithm for PM2. 5 concentration prediction

Y Xing, J Yue, C Chen, Y Xiang, Y Chen, M Shi - Applied Sciences, 2019 - mdpi.com
Accurate PM2. 5 concentration prediction is crucial for protecting public health and
improving air quality. As a popular deep learning model, deep belief network (DBN) for PM2 …

PM2. 5 concentration modeling and prediction by using temperature-based deep belief network

H Xing, G Wang, C Liu, M Suo - Neural Networks, 2021 - Elsevier
Air quality prediction is a global hot issue, and PM 2.5 is an important factor affecting air
quality. Due to complicated causes of formation, PM 2.5 prediction is a thorny and …

[HTML][HTML] PM2.5 Concentration Prediction Based on LightGBM Optimized by Adaptive Multi-Strategy Enhanced Sparrow Search Algorithm

X Liu, K Zhao, Z Liu, L Wang - Atmosphere, 2023 - mdpi.com
The atmospheric environment is of great importance to human health. However, its
influencing factors are complex and variable. An efficient technique is required to more …

PM2. 5 prediction using genetic algorithm-based feature selection and encoder-decoder model

MH Nguyen, P Le Nguyen, K Nguyen… - IEEE …, 2021 - ieeexplore.ieee.org
The concentration of fine particulate matter (PM2. 5), which represents inhalable particles
with diameters of 2.5 micrometers and smaller, is a vital air quality index. Such particles can …

[HTML][HTML] Forecasting of PM2.5 Concentration in Beijing Using Hybrid Deep Learning Framework Based on Attention Mechanism

D Li, J Liu, Y Zhao - Applied Sciences, 2022 - mdpi.com
Air pollution has become a critical factor affecting the health of human beings. Forecasting
the trend of air pollutants will be of considerable help to public health, including improving …

[HTML][HTML] Pm2. 5 concentration prediction based on cnn-bilstm and attention mechanism

J Zhang, Y Peng, B Ren, T Li - Algorithms, 2021 - mdpi.com
The concentration of PM2. 5 is an important index to measure the degree of air pollution.
When it exceeds the standard value, it is considered to cause pollution and lower the air …

[HTML][HTML] PM2. 5 concentration prediction using weighted CEEMDAN and improved LSTM neural network

L Zhang, J Liu, Y Feng, P Wu, P He - Environmental Science and Pollution …, 2023 - Springer
As the core of pollution prevention and management, accurate PM2. 5 concentration
prediction is crucial for human survival. However, due to the nonstationarity and nonlinearity …

[HTML][HTML] An improved hybrid transfer learning-based deep learning model for PM2. 5 concentration prediction

J Ni, Y Chen, Y Gu, X Fang, P Shi - Applied Sciences, 2022 - mdpi.com
With the improvement of the living standards of the residents, it is a very important and
challenging task to continuously improve the accuracy of PM2. 5 (particulate matter less than …

An optimized hybrid deep learning model for PM2.5 and O3 concentration prediction

J Hu, Y Chen, W Wang, S Zhang, C Cui, W Ding… - Air Quality, Atmosphere …, 2023 - Springer
As people focus more on environmental protection, air quality prediction plays an
increasingly important role in reducing pollution hazards. Both fine particulate matter (PM2 …

[HTML][HTML] An Improved Attention-Based Integrated Deep Neural Network for PM2.5 Concentration Prediction

P Shi, X Fang, J Ni, J Zhu - Applied Sciences, 2021 - mdpi.com
The air quality prediction is a very important and challenging task, especially PM 2.5
(particles with diameter less than 2.5 μm) concentration prediction. To improve the accuracy …