Cited by A Novel Fuzzified Markov Chain Approach to Model Monthly River Discharge

SMGformer: integrating STL and multi-head self-attention in deep learning model for multi-step runoff forecasting DOI

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

Study on the evolution of ecological flow in river and its guarantee degree during different hydrological periods DOI

Xue Chen, Qin Li, Zhuo Jia

et al.

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 144761 - 144761

Published: Jan. 1, 2025

Language: Английский

Citations

A runoff prediction approach based on machine learning, ensemble forecasting and error correction: A case study of source area of Yellow River DOI

Jingyang Wang, Xiang Li,

Ruiyan Wu

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133190 - 133190

Published: March 1, 2025

Language: Английский

Citations

A novel daily runoff forecasting model based on global features and enhanced local feature interpretation DOI

Dongmei Xu,

Yang-hao Hong,

Wenchuan Wang

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132227 - 132227

Published: Oct. 1, 2024

Language: Английский

Citations

Combined multi-component composite time series power prediction model for distributed energy systems based on STL data decomposition DOI

T. J. Yang,

Liansheng Huang,

Peng Fu

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117299 - 117299

Published: April 1, 2025

Language: Английский

Citations

WaveTransTimesNet: an enhanced deep learning monthly runoff prediction model based on wavelet transform and transformer architecture DOI

Dongmei Xu, Zong Li, Wenchuan Wang

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 24, 2025

Language: Английский

Citations

Enhancing monthly runoff prediction: a data-driven framework integrating variational mode decomposition, enhanced artificial rabbit optimization, support vector regression, and error correction DOI

Ning He, Wenchuan Wang

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 17, 2025

Language: Английский

Citations

A Novel Fuzzified Markov Chain Approach to Model Monthly River Discharge DOI

Mohammad Mahdi Dorafshan, Mohammad H. Golmohammadi,

Keyvan Asghari

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 10, 2024

Language: Английский

Citations