Feature-driven hybrid attention learning for accurate water quality prediction DOI
Xuan Yao, Zeshui Xu, Tianyu Ren

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127160 - 127160

Published: March 1, 2025

Language: Английский

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

40
Risk analysis and assessment of water resource carrying capacity based on weighted gray model with improved entropy weighting method in the central plains region of China DOI Creative Commons

Qiran Song,

Zhaocai Wang, Tunhua Wu

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 160, P. 111907 - 111907

Published: March 1, 2024

The issue of global water shortage is a serious concern. scientific evaluation resource carrying capacity (WRCC) serves as the foundation for implementing measures to protect resources. In addition, most studies are based on analysis and research regional WRCC from aspects quantity quality. There few four resources endowment conditions, society, economy ecological environment, which difficult scientifically accurately reflect by systems. Therefore, it necessary conduct deeper discussion Analysis this topic. This study presents index system corresponding ranking criteria 20 influencing factors aspects: (WRE), economy, environment. combining improved entropy weighting method (EWM) with gray correlation analysis, weighted technique order preference similarity an ideal solution (TOPSIS) model proposed analyzing assessing risk. Finally, area 2012 2021 comprehensively evaluated in central plains region China (CPROC) example. results show that comprehensive obtained multi-year average value 0.2935, CPROC generally grade III status. Beijing 0.345, Henan 0.397. overall degree state V shortage, Shaanxi IV Tianjin Shanxi relatively good. provides basis methodological guidance sustainable utilization healthy socio-economic performance CPROC.

Language: Английский

Citations

31
A Novel Runoff Prediction Model Based on Support Vector Machine and Gate Recurrent unit with Secondary Mode Decomposition DOI
Jinghan Dong, Zhaocai Wang, Tunhua Wu

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(5), P. 1655 - 1674

Published: Feb. 6, 2024

Language: Английский

Citations

29
A secondary modal decomposition ensemble deep learning model for groundwater level prediction using multi-data DOI

Xuefei Cui,

Zhaocai Wang, Nannan Xu

et al.

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 175, P. 105969 - 105969

Published: Feb. 7, 2024

Language: Английский

Citations

23
Enhancing prediction of dissolved oxygen over Santa Margarita River: Long short-term memory incorporated with multi-objective observer-teacher-learner optimization DOI
Siyamak Doroudi, Yusef Kheyruri, Ahmad Sharafati

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 70, P. 106969 - 106969

Published: Jan. 11, 2025

Language: Английский

Citations

1
Dissolved Oxygen Forecasting for Lake Erie’s Central Basin Using Hybrid Long Short-Term Memory and Gated Recurrent Unit Networks DOI Open Access

Daiwei Pan,

Yue Zhang, Ying Deng

et al.

Water, Journal Year: 2024, Volume and Issue: 16(5), P. 707 - 707

Published: Feb. 28, 2024

Dissolved oxygen (DO) concentration is a pivotal determinant of water quality in freshwater lake ecosystems. However, rapid population growth and discharge polluted wastewater, urban stormwater runoff, agricultural non-point source pollution runoff have triggered significant decline DO levels Lake Erie other lakes located populated temperate regions the globe. Over eleven million people rely on Erie, which has been adversely impacted by anthropogenic stressors resulting deficient concentrations near bottom Erie’s Central Basin for extended periods. In past, hybrid long short-term memory (LSTM) models successfully used time-series forecasting rivers ponds. prediction errors tend to grow significantly with period. Therefore, this research aimed improve accuracy taking advantage real-time (water temperature concentration) monitoring network establish temporal spatial links between adjacent stations. We developed LSTM that combine LSTM, convolutional neuron (CNN-LSTM), CNN gated recurrent unit (CNN-GRU) models, (ConvLSTM) forecast near-bottom Basin. These their capacity handle complicated datasets variability. can serve as accurate reliable tools help environmental protection agencies better access manage health these vital Following analysis 21-site dataset 2020 2021, ConvLSTM model emerged most reliable, boasting an MSE 0.51 mg/L, MAE 0.42 R-squared 0.95 over 12 h range. The foresees future hypoxia Erie. Notably, site 713 holds significance indicated outcomes derived from Shapley additive explanations (SHAP).

Language: Английский

Citations

6
An optimized decomposition integration model for deterministic and probabilistic air pollutant concentration prediction considering influencing factors DOI
Fan Yang, Guangqiu Huang

Atmospheric Pollution Research, Journal Year: 2024, Volume and Issue: 15(7), P. 102144 - 102144

Published: April 4, 2024

Language: Английский

Citations

5
A risk warning method for steady-state power quality based on VMD-LSTM and fuzzy model DOI Creative Commons

Yu Shen,

Wei Hu,

Mingqi Dong

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(9), P. e30597 - e30597

Published: May 1, 2024

The risk warning for steady-state power quality in the grid is essential its prevention and management. However, current methods fall short predicting trend while accounting potential risks. Consequently, this study introduces a novel method utilizing VMD-LSTM fuzzy model. Firstly, index prediction based on variational mode decomposition (VMD) long short-term memory (LSTM) proposed. This approach significantly enhances accuracy. Secondly, incorporating kernel density estimation (KDE) model proposed, which systematically addresses uncertainty associated with To validate effectiveness practicality of proposed method, experiments are conducted using field monitoring data from residential load southern China. results affirm reliability applicability method. simulation show that median error indexes by 5.03% during evaluated time period, accuracy mostly maintained above 90%.

Language: Английский

Citations

5
Multi-step ahead dissolved oxygen concentration prediction based on knowledge guided ensemble learning and explainable artificial intelligence DOI
Tunhua Wu, Zhaocai Wang, Jinghan Dong

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 636, P. 131297 - 131297

Published: May 9, 2024

Language: Английский

Citations

5
Enhancing water quality monitoring through the integration of deep learning neural networks and fuzzy method DOI

Marzieh Mokarram,

Hamid Reza Pourghasemi, Tam Minh Pham

et al.

Marine Pollution Bulletin, Journal Year: 2024, Volume and Issue: 206, P. 116698 - 116698

Published: July 12, 2024

Language: Английский

Citations

5