A Mamba-based method for multi-feature water quality prediction fusing dual denoising and attention enhancement DOI

Xianbao Tan,

Yulong Bai, Xin Yue

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133424 - 133424

Published: April 1, 2025

Language: Английский

Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions DOI Creative Commons
Kumar Puran Tripathy, Ashok K. Mishra

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 628, P. 130458 - 130458

Published: Nov. 15, 2023

Language: Английский

Citations

95
Deep learning based data-driven model for detecting time-delay water quality indicators of wastewater treatment plant influent DOI

Yituo Zhang,

Chaolin Li,

Hengpan Duan

et al.

Chemical Engineering Journal, Journal Year: 2023, Volume and Issue: 467, P. 143483 - 143483

Published: May 15, 2023

Language: Английский

Citations

65
Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms DOI
Swapan Talukdar,

Shahfahad,

Shakeel Ahmed

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 406, P. 136885 - 136885

Published: April 3, 2023

Language: Английский

Citations

62
Interpretable prediction, classification and regulation of water quality: A case study of Poyang Lake, China DOI
Zhiyuan Yao, Zhaocai Wang,

Jinghan Huang

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175407 - 175407

Published: Aug. 9, 2024

Language: Английский

Citations

24
Water quality prediction in the Yellow River source area based on the DeepTCN-GRU model DOI
Qingqing Tian,

Wei Luo,

Lei Guo

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 59, P. 105052 - 105052

Published: March 1, 2024

Language: Английский

Citations

21
Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods DOI Creative Commons
Xianhe Wang, Ying Li, Qian Qiao

et al.

Entropy, Journal Year: 2023, Volume and Issue: 25(8), P. 1186 - 1186

Published: Aug. 9, 2023

In the context of escalating global environmental concerns, importance preserving water resources and upholding ecological equilibrium has become increasingly apparent. As a result, monitoring prediction quality have emerged as vital tasks in achieving these objectives. However, ensuring accuracy dependability proven to be challenging endeavor. To address this issue, study proposes comprehensive weight-based approach that combines entropy weighting with Pearson correlation coefficient select crucial features prediction. This effectively considers both feature information content, avoiding excessive reliance on single criterion for selection. Through utilization approach, evaluation contribution was achieved, thereby minimizing subjective bias uncertainty. By striking balance among various factors, stronger greater content can selected, leading improved robustness feature-selection process. Furthermore, explored several machine learning models prediction, including Support Vector Machines (SVMs), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM). SVM exhibited commendable performance predicting Dissolved Oxygen (DO), showcasing excellent generalization capabilities high accuracy. MLP demonstrated its strength nonlinear modeling performed well multiple parameters. Conversely, RF XGBoost relatively inferior contrast, LSTM model, recurrent neural network specialized processing time series data, exceptional abilities It captured dynamic patterns present offering stable accurate predictions

Language: Английский

Citations

34
A coupled model to improve river water quality prediction towards addressing non-stationarity and data limitation DOI
Shengyue Chen, Jinliang Huang, Peng Wang

et al.

Water Research, Journal Year: 2023, Volume and Issue: 248, P. 120895 - 120895

Published: Nov. 20, 2023

Language: Английский

Citations

25
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
Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis DOI
Mahmood Fooladi, Mohammad Reza Nikoo, Rasoul Mirghafari

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 362, P. 121259 - 121259

Published: June 1, 2024

Language: Английский

Citations

11
Deep learning model based on coupled SWAT and interpretable methods for water quality prediction under the influence of non-point source pollution DOI
Juan Huan,

Yixiong Fan,

Xiangen Xu

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 231, P. 109985 - 109985

Published: Jan. 23, 2025

Language: Английский

Citations

1