Cyberinfrastructure for sourcing and processing ecological data DOI
Friedrich Recknagel

Ecological Informatics, Journal Year: 2023, Volume and Issue: 75, P. 102039 - 102039

Published: March 2, 2023

Language: Английский

Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer DOI
Yuqi Wang, Hongcheng Wang,

Yunpeng Song

et al.

Water Research, Journal Year: 2023, Volume and Issue: 246, P. 120676 - 120676

Published: Sept. 28, 2023

Language: Английский

Citations

44
Ecological disturbances and abundance of anthropogenic pollutants in the aquatic ecosystem: Critical review of impact assessment on the aquatic animals DOI Creative Commons

S. Thanigaivel,

Sundaram Vickram,

Nibedita Dey

et al.

Chemosphere, Journal Year: 2022, Volume and Issue: 313, P. 137475 - 137475

Published: Dec. 14, 2022

Language: Английский

Citations

56
Current status and prospects of algal bloom early warning technologies: A Review DOI
X.L. Xiao, Yazhou Peng, Wei Zhang

et al.

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 349, P. 119510 - 119510

Published: Nov. 9, 2023

Language: Английский

Citations

30
A state-of-the-art review of long short-term memory models with applications in hydrology and water resources DOI
Zhong-kai Feng, J. Zhang, Wen-jing Niu

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112352 - 112352

Published: Oct. 1, 2024

Language: Английский

Citations

9
Attention-based Deep learning Models for Predicting Anomalous Shock of Wastewater Treatment Plants DOI

Yituo Zhang,

Jihong Wang, Chaolin Li

et al.

Water Research, Journal Year: 2025, Volume and Issue: 275, P. 123192 - 123192

Published: Jan. 23, 2025

Language: Английский

Citations

1
Enhancing short-term algal bloom forecasting through an anti-mimicking hybrid deep learning method DOI
Ya-Qin Zhang,

Yichong Wang,

Huihuang Chen

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 379, P. 124832 - 124832

Published: March 10, 2025

Language: Английский

Citations

1
Adaptive prediction for effluent quality of wastewater treatment plant: Improvement with a dual-stage attention-based LSTM network DOI
Tong An,

Kuanliang Feng,

Peijin Cheng

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 359, P. 120887 - 120887

Published: April 27, 2024

Language: Английский

Citations

8
Modeling of algal blooms: Advances, applications and prospects DOI

Yichong Wang,

Chao Xu, Qianru Lin

et al.

Ocean & Coastal Management, Journal Year: 2024, Volume and Issue: 255, P. 107250 - 107250

Published: June 24, 2024

Language: Английский

Citations

8
Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake DOI
Lan Wang, Kun Shan, Yi Yang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 922, P. 171009 - 171009

Published: Feb. 24, 2024

Language: Английский

Citations

7
One-Week-Ahead Prediction of Cyanobacterial Harmful Algal Blooms in Iowa Lakes DOI

Paul Villanueva,

Jihoon Yang,

Lorien Radmer

et al.

Environmental Science & Technology, Journal Year: 2023, Volume and Issue: 57(49), P. 20636 - 20646

Published: Nov. 27, 2023

Cyanobacterial harmful algal blooms (CyanoHABs) pose serious risks to inland water resources. Despite advancements in our understanding of associated environmental factors and modeling efforts, predicting CyanoHABs remains challenging. Leveraging an integrated quality data collection effort Iowa lakes, this study aimed identify with hazardous microcystin levels develop one-week-ahead predictive classification models. Using samples from 38 lakes collected between 2018 2021, feature selection was conducted considering both linear nonlinear properties. Subsequently, we developed three model types (Neural Network, XGBoost, Logistic Regression) different sampling strategies using the nine selected variables (mcyA_M, TKN, % hay/pasture, pH, mcyA_M:16S, developed, DOC, dewpoint temperature, ortho-P). Evaluation metrics demonstrated strong performance Neural Network oversampling (ROC-AUC 0.940, accuracy 0.861, sensitivity 0.857, specificity LR+ 5.993, 1/LR– 5.993), as well XGBoost downsampling 0.944, 0.831, 0.928, 0.833, 5.557, 11.569). This exhibited intricacies limited class imbalances, underscoring importance continuous monitoring improve accuracy. Also, methodologies employed can serve meaningful references for researchers tackling similar challenges diverse environments.

Language: Английский

Citations

11