Cargando…
Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model
In this study, to further improve the prediction accuracy of coal mine gas concentration and thereby preventing gas accidents and improving coal mine safety management, the standard whale optimisation algorithm’s (WOA) susceptibility to falling into local optima, slow convergence speed, and low pred...
Autores principales: | Xu, Ningke, Wang, Xiangqian, Meng, Xiangrui, Chang, Haoqian |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9230321/ https://www.ncbi.nlm.nih.gov/pubmed/35746193 http://dx.doi.org/10.3390/s22124412 |
Ejemplares similares
-
Prediction of PM(2.5) concentration based on the CEEMDAN-RLMD-BiLSTM-LEC model
por: Guo, Qiao, et al.
Publicado: (2023) -
PM2.5 concentration prediction using weighted CEEMDAN and improved LSTM neural network
por: Zhang, Li, et al.
Publicado: (2023) -
Temperature Prediction of Seasonal Frozen Subgrades Based on CEEMDAN-LSTM Hybrid Model
por: Chen, Liyue, et al.
Publicado: (2022) -
Prediction of sea ice area based on the CEEMDAN-SO-BiLSTM model
por: Guo, Qiao, et al.
Publicado: (2023) -
Daily flow prediction of the Huayuankou hydrometeorological station based on the coupled CEEMDAN–SE–BiLSTM model
por: Li, Haiyang, et al.
Publicado: (2023)