Network traffic grant classification based on 1DCNN-TCN-GRU hybrid model DOI
Lina Mo, Xiaogang Qi, Lifang Liu

et al.

Applied Intelligence, Journal Year: 2024, Volume and Issue: 54(6), P. 4834 - 4847

Published: March 1, 2024

Language: Английский

An evolutionary deep learning model based on EWKM, random forest algorithm, SSA and BiLSTM for building energy consumption prediction DOI
Lei Lei, Suola Shao,

Lixia Liang

et al.

Energy, Journal Year: 2023, Volume and Issue: 288, P. 129795 - 129795

Published: Nov. 29, 2023

Language: Английский

Citations

44
Spatio-temporal deep learning model for accurate streamflow prediction with multi-source data fusion DOI
Zhaocai Wang, Nannan Xu, Xiaoguang Bao

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 178, P. 106091 - 106091

Published: May 28, 2024

Language: Английский

Citations

41
A novel DWTimesNet-based short-term multi-step wind power forecasting model using feature selection and auto-tuning methods DOI
Chu Zhang, Yuhan Wang,

Yongyan Fu

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 301, P. 118045 - 118045

Published: Jan. 5, 2024

Language: Английский

Citations

25
Enhancing state of charge and state of energy estimation in Lithium-ion batteries based on a TimesNet model with Gaussian data augmentation and error correction DOI
Chu Zhang, Yue Zhang, Zhengbo Li

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 359, P. 122669 - 122669

Published: Jan. 28, 2024

Language: Английский

Citations

22
Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models DOI
Gang Li, Zhangkang Shu,

Miaoli Lin

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141228 - 141228

Published: Feb. 13, 2024

Language: Английский

Citations

13
SMGformer: integrating STL and multi-head self-attention in deep learning model for multi-step runoff forecasting DOI Creative Commons
Wenchuan Wang, M. H. Gu,

Yang-hao Hong

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 9, 2024

Accurate runoff forecasting is of great significance for water resource allocation flood control and disaster reduction. However, due to the inherent strong randomness sequences, this task faces significant challenges. To address challenge, study proposes a new SMGformer forecast model. The model integrates Seasonal Trend decomposition using Loess (STL), Informer's Encoder layer, Bidirectional Gated Recurrent Unit (BiGRU), Multi-head self-attention (MHSA). Firstly, in response nonlinear non-stationary characteristics sequence, STL used extract sequence's trend, period, residual terms, multi-feature set based on 'sequence-sequence' constructed as input model, providing foundation subsequent models capture evolution runoff. key features are then captured layer. Next, BiGRU layer learn temporal information these features. further optimize output MHSA mechanism introduced emphasize impact important information. Finally, accurate achieved by transforming through Fully connected verify effectiveness proposed monthly data from two hydrological stations China selected, eight compare performance results show that compared with Informer 1th step MAE decreases 42.2% 36.6%, respectively; RMSE 37.9% 43.6% NSE increases 0.936 0.975 0.487 0.837, respectively. In addition, KGE at 3th 0.960 0.805, both which can maintain above 0.8. Therefore, accurately sequence extend effective period

Language: Английский

Citations

9
Urban real-time rainfall-runoff prediction using adaptive SSA-decomposition with dual attention DOI
Yuan Tian, Weiming Fu, Yi Xiang

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132701 - 132701

Published: Jan. 1, 2025

Language: Английский

Citations

1
Hybrid solar radiation forecasting model with temporal convolutional network using data decomposition and improved artificial ecosystem-based optimization algorithm DOI
Yuhan Wang, Chu Zhang,

Yongyan Fu

et al.

Energy, Journal Year: 2023, Volume and Issue: 280, P. 128171 - 128171

Published: June 18, 2023

Language: Английский

Citations

20
A hybrid annual runoff prediction model using echo state network and gated recurrent unit based on sand cat swarm optimization with Markov chain error correction method DOI Creative Commons
Jun Wang, Wenchuan Wang,

Xiao-xue Hu

et al.

Journal of Hydroinformatics, Journal Year: 2024, Volume and Issue: 26(6), P. 1425 - 1453

Published: May 16, 2024

ABSTRACT This study proposes a hybrid model based on the combination of Sand Cat Swarm Optimization (SCSO), Echo State Network (ESN), Gated Recurrent Unit (GRU), Least Squares Method (LSM), and Markov Chain (MC) to improve accuracy annual runoff prediction. Firstly, conduct correlation analysis multi-factor data related determine input model. Secondly, SCSO algorithm is used optimize parameters ESN GRU models, SCSO-ESN SCSO-GRU models are established. Next, prediction results these two coupled using LSM obtain preliminary SCSO-ESN-GRU Finally, initial corrected for errors MC get final results. Choose Changshui Station Lanxi experiments, evaluate predictive performance through five evaluation indicators. The show that combined by achieved optimal at both experimental stations. emphasizes chain correction can significantly prediction, providing reliable basis predicting in complex watersheds.

Language: Английский

Citations

8
Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting DOI
Chengshuai Liu, Wenzhong Li, Caihong Hu

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 362, P. 121260 - 121260

Published: June 1, 2024

Language: Английский

Citations

8