Control approach and evaluation framework of scaling in drinking water distribution systems: A review DOI
Changgeng Li,

Cheng Liu,

Wei‐Bin Xu

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 948, P. 174836 - 174836

Published: Oct. 1, 2024

Language: Английский

A novel hybrid model for water quality prediction based on VMD and IGOA optimized for LSTM DOI
Zhaocai Wang, Qingyu Wang, Tunhua Wu

et al.

Frontiers of Environmental Science & Engineering, Journal Year: 2023, Volume and Issue: 17(7)

Published: Feb. 13, 2023

Language: Английский

Citations

85

Pollution source identification and abatement for water quality sections in Huangshui River basin, China DOI
Yonggui Wang, X. Ding, Yan Chen

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 344, P. 118326 - 118326

Published: June 15, 2023

Language: Английский

Citations

16

A review of wetland vulnerability assessment and monitoring in semi-arid environments of sub-Saharan Africa DOI

Thandekile Dube,

Timothy Dube, Thomas Marambanyika

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2023, Volume and Issue: 132, P. 103473 - 103473

Published: Aug. 22, 2023

Language: Английский

Citations

15

An Efficient Water Quality Prediction and Assessment Method Based on the Improved Deep Belief Network—Long Short-Term Memory Model DOI Open Access
Zhiyao Zhao,

Bing Fan,

Yuqin Zhou

et al.

Water, Journal Year: 2024, Volume and Issue: 16(10), P. 1362 - 1362

Published: May 11, 2024

The accuracy of water quality prediction and assessment has always been the focus environmental departments. However, due to high complexity systems, existing methods struggle capture future internal dynamic changes in based on current data. In view this, this paper proposes a data-driven approach combine an improved deep belief network (DBN) long short-term memory (LSTM) model for assessment, avoiding constructing mechanism quality. Firstly, using Gaussian Restricted Boltzmann Machines (GRBMs) construct DBN, better ability extract continuous data features compared classical DBN. Secondly, extracted time-series are input into LSTM improve predicting accuracy. Finally, errors, noise that randomly follows distribution is added results predicted values, probability being at level calculated through multiple evolutionary computations complete assessment. Numerical experiments have shown our proposed algorithm greater algorithms challenging scenarios.

Language: Английский

Citations

5

Intensified effect of nitrogen forms on dominant phytoplankton species succession by climate change DOI
Xuemei Liu, Jingjie Zhang, Yanfeng Wu

et al.

Water Research, Journal Year: 2024, Volume and Issue: 264, P. 122214 - 122214

Published: Aug. 3, 2024

Language: Английский

Citations

5

Initial water rights allocation of Industry in the Yellow River basin driven by high-quality development DOI
Xiang-nan Chen, Fang Li,

Fengping Wu

et al.

Ecological Modelling, Journal Year: 2023, Volume and Issue: 477, P. 110272 - 110272

Published: Jan. 4, 2023

Language: Английский

Citations

12

Backwater makes the tributaries of large river becoming phosphorus “sink” DOI

Bingfen Cheng,

Yuan Zhang, Rui Xia

et al.

Water Research, Journal Year: 2024, Volume and Issue: 261, P. 122012 - 122012

Published: June 28, 2024

Language: Английский

Citations

4

Characterization and Modeling of Harmful Algal Blooms: A Review DOI
Rao S. Govindaraju, Rao S. Govindaraju

Journal of Hydraulic Engineering, Journal Year: 2025, Volume and Issue: 151(2)

Published: Jan. 2, 2025

Language: Английский

Citations

0

Impacts of watershed nutrient loads on eutrophication risks under multiple socio-economic development scenarios in the Pearl River Estuary, China DOI
Ying Yang, Yujian Zhang, Jixian Zhang

et al.

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 145133 - 145133

Published: Feb. 1, 2025

Language: Английский

Citations

0

Coupled hydrologic, hydraulic, and surface water quality models for pollution management in urban–rural areas DOI Creative Commons
Matteo Masi, Daniele Masseroni, Fabio Castelli

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133172 - 133172

Published: March 1, 2025

Language: Английский

Citations

0