251 related articles for article (PubMed ID: 36554314)
1. CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems.
Zeng L; Li Z; Yang J; Xu X
Int J Environ Res Public Health; 2022 Dec; 19(24):. PubMed ID: 36554314
[TBL] [Abstract][Full Text] [Related]
2. Temperature Prediction of Seasonal Frozen Subgrades Based on CEEMDAN-LSTM Hybrid Model.
Chen L; Liu X; Zeng C; He X; Chen F; Zhu B
Sensors (Basel); 2022 Aug; 22(15):. PubMed ID: 35957299
[TBL] [Abstract][Full Text] [Related]
3. Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model.
Xu N; Wang X; Meng X; Chang H
Sensors (Basel); 2022 Jun; 22(12):. PubMed ID: 35746193
[TBL] [Abstract][Full Text] [Related]
4. Empirical mode decomposition based long short-term memory neural network forecasting model for the short-term metro passenger flow.
Chen Q; Wen D; Li X; Chen D; Lv H; Zhang J; Gao P
PLoS One; 2019; 14(9):e0222365. PubMed ID: 31509599
[TBL] [Abstract][Full Text] [Related]
5. A novel model based on CEEMDAN, IWOA, and LSTM for ultra-short-term wind power forecasting.
Yang S; Yuan A; Yu Z
Environ Sci Pollut Res Int; 2023 Jan; 30(5):11689-11705. PubMed ID: 36098919
[TBL] [Abstract][Full Text] [Related]
6. Dynamic real-time forecasting technique for reclaimed water volumes in urban river environmental management.
Zhang L; Wang C; Hu W; Wang X; Wang H; Sun X; Ren W; Feng Y
Environ Res; 2024 May; 248():118267. PubMed ID: 38244969
[TBL] [Abstract][Full Text] [Related]
7. Computational and Mathematical Methods in Medicine Prediction of COVID-19 in BRICS Countries: An Integrated Deep Learning Model of CEEMDAN-R-ILSTM-Elman.
Zhao Q; Zheng Z
Comput Math Methods Med; 2022; 2022():1566727. PubMed ID: 35419081
[TBL] [Abstract][Full Text] [Related]
8. An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic.
Jiao F; Huang L; Song R; Huang H
Sensors (Basel); 2021 Sep; 21(17):. PubMed ID: 34502841
[TBL] [Abstract][Full Text] [Related]
9. Multi-step forecasting of dissolved oxygen in River Ganga based on CEEMDAN-AdaBoost-BiLSTM-LSTM model.
Pant N; Toshniwal D; Gurjar BR
Sci Rep; 2024 May; 14(1):11199. PubMed ID: 38755217
[TBL] [Abstract][Full Text] [Related]
10. PM2.5 concentration prediction using weighted CEEMDAN and improved LSTM neural network.
Zhang L; Liu J; Feng Y; Wu P; He P
Environ Sci Pollut Res Int; 2023 Jun; 30(30):75104-75115. PubMed ID: 37213020
[TBL] [Abstract][Full Text] [Related]
11. Prediction model of land surface settlement deformation based on improved LSTM method: CEEMDAN-ICA-AM-LSTM (CIAL) prediction model.
Zhu S; Qin Y; Meng X; Xie L; Zhang Y; Yuan Y
PLoS One; 2024; 19(3):e0298524. PubMed ID: 38452152
[TBL] [Abstract][Full Text] [Related]
12. A novel hybrid model for six main pollutant concentrations forecasting based on improved LSTM neural networks.
Xu S; Li W; Zhu Y; Xu A
Sci Rep; 2022 Aug; 12(1):14434. PubMed ID: 36002466
[TBL] [Abstract][Full Text] [Related]
13. A new denoising approach based on mode decomposition applied to the stock market time series: 2LE-CEEMDAN.
Akşehir ZD; Kılıç E
PeerJ Comput Sci; 2024; 10():e1852. PubMed ID: 38435596
[TBL] [Abstract][Full Text] [Related]
14. A hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit.
Jiao J; Ma Q; Liu F; Zhao L; Huang S
Environ Geochem Health; 2024 Mar; 46(4):127. PubMed ID: 38483668
[TBL] [Abstract][Full Text] [Related]
15. A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction.
Tian H; Yuan H; Yan K; Guo J
PeerJ Comput Sci; 2024; 10():e2048. PubMed ID: 38855216
[TBL] [Abstract][Full Text] [Related]
16. Daily flow prediction of the Huayuankou hydrometeorological station based on the coupled CEEMDAN-SE-BiLSTM model.
Li H; Zhang X; Sun S; Wen Y; Yin Q
Sci Rep; 2023 Nov; 13(1):18915. PubMed ID: 37919397
[TBL] [Abstract][Full Text] [Related]
17. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.
Zhang X; Zhang Q; Zhang G; Nie Z; Gui Z; Que H
Int J Environ Res Public Health; 2018 May; 15(5):. PubMed ID: 29883381
[TBL] [Abstract][Full Text] [Related]
18. IPSO-LSTM hybrid model for predicting online public opinion trends in emergencies.
Mu G; Liao Z; Li J; Qin N; Yang Z
PLoS One; 2023; 18(10):e0292677. PubMed ID: 37815983
[TBL] [Abstract][Full Text] [Related]
19. Prediction of sea ice area based on the CEEMDAN-SO-BiLSTM model.
Guo Q; Zhang H; Zhang Y; Jiang X
PeerJ; 2023; 11():e15748. PubMed ID: 37483978
[TBL] [Abstract][Full Text] [Related]
20. A multi-scale evolutionary deep learning model based on CEEMDAN, improved whale optimization algorithm, regularized extreme learning machine and LSTM for AQI prediction.
Ji C; Zhang C; Hua L; Ma H; Nazir MS; Peng T
Environ Res; 2022 Dec; 215(Pt 1):114228. PubMed ID: 36084674
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
