Cargando…
A novel hybrid model for six main pollutant concentrations forecasting based on improved LSTM neural networks
In recent years, air pollution has become a factor that cannot be ignored, affecting human lives and health. The distribution of high-density populations and high-intensity development and construction have accentuated the problem of air pollution in China. To accelerate air pollution control and ef...
Autores principales: | Xu, Shenyi, Li, Wei, Zhu, Yuhan, Xu, Aiting |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9402967/ https://www.ncbi.nlm.nih.gov/pubmed/36002466 http://dx.doi.org/10.1038/s41598-022-17754-3 |
Ejemplares similares
-
Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks
por: Awan, Faraz Malik, et al.
Publicado: (2020) -
PM2.5 concentration prediction using weighted CEEMDAN and improved LSTM neural network
por: Zhang, Li, et al.
Publicado: (2023) -
Forecasting Hazard Level of Air Pollutants Using LSTM’s
por: Gul, Saba, et al.
Publicado: (2020) -
Forecast of the Employment Situation of College Graduates Based on the LSTM Neural Network
por: Li, Xing, et al.
Publicado: (2021) -
Retracted: Forecast of the Employment Situation of College Graduates Based on the LSTM Neural Network
por: Intelligence and Neuroscience, Computational
Publicado: (2023)