A novel interpretable hybrid model for multi-step ahead dissolved oxygen forecasting in the Mississippi River basin DOI

Hassan M. Alwan,

Mehdi Mohammadi Ghaleni, Mahnoosh Moghaddasi

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 28, 2024

Language: Английский

Enhancing Tree-Based Machine Learning for Chlorophyll-a Prediction in Coastal Seawater Through Spatiotemporal Feature Integration DOI
Tongcun Liu, Geum Bong Yu, Hoi‐Hin Kwok

et al.

Marine Environmental Research, Journal Year: 2025, Volume and Issue: 209, P. 107170 - 107170

Published: April 24, 2025

Language: Английский

Citations

0
Predicting unseen chub mackerel densities through spatiotemporal machine learning: Indications of potential hyperdepletion in catch-per-unit-effort due to fishing ground contraction DOI Creative Commons

Shota Kunimatsu,

Hiroyuki Kurota, Soyoka Muko

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 85, P. 102944 - 102944

Published: Dec. 9, 2024

Language: Английский

Citations

3
Dissolved Oxygen Prediction in the Dianchi River Basin with Explainable Artificial Intelligence based on Physical Prior Knowledge DOI
Tunhua Wu, Xi Chen, Jinghan Dong

et al.

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: 188, P. 106412 - 106412

Published: March 5, 2025

Language: Английский

Citations

0
Dissolved oxygen prediction in the Taiwan Strait with the attention-based multi-teacher knowledge distillation model DOI
Lei Chen,

Lin Ye,

Minquan Guo

et al.

Ocean & Coastal Management, Journal Year: 2025, Volume and Issue: 265, P. 107628 - 107628

Published: March 22, 2025

Language: Английский

Citations

0
Assessing the impacts of cascade reservoirs on Pearl River environmental status using machine learning and satellite-derived chlorophyll-a concentrations DOI

Z.H. Li,

Xiankun Yang,

Lishan Ran

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 382, P. 125406 - 125406

Published: April 18, 2025

Language: Английский

Citations

0
Research on Prediction of Marine Dissolved Oxygen Concentration Based on Modal Decomposition DOI
Yan Liu, Yupeng Zhao,

F. Liu

et al.

Lecture notes in civil engineering, Journal Year: 2025, Volume and Issue: unknown, P. 361 - 376

Published: Jan. 1, 2025

Language: Английский

Citations

0
A long-term multivariate time series prediction model for dissolved oxygen DOI Creative Commons

Jingzhe Hu,

Peixuan Wang,

Dashe Li

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102695 - 102695

Published: June 20, 2024

Accurate and efficient long-term prediction of marine dissolved oxygen (DO) is crucial for the sustainable development aquaculture. However, multidimensional time dependency lag effects chemical variables present significant challenges when handling multiple inputs in univariate tasks. To address these issues, we designed a multivariate time-series model (LMFormer) based on Transformer architecture. The proposed decomposition strategy effectively leverages feature information at different scales, thereby reducing loss critical information. Additionally, dynamic variable selection gating mechanism was to optimize collinearity problem data extraction process. Finally, an two-stage attention architecture capture long-range dependencies between features. This study conducted high-precision 7-day advance DO predictions two case studies, environmentally stable Shandong Peninsula China San Juan Islands United States, which are affected by extreme conditions such as ocean currents. experimental results demonstrate superior performance generalizability model. In case, mean absolute error (MAE), root square (RMSE), coefficient determination (R2), Kling–Gupta efficiency (KGE) reached 0.0159, 0.126, 0.9743, 0.9625, respectively. MAE reduced average 42.34% compared that baseline model, RMSE 24.57%, R2 increased 22.54%, KGE improved 12.04%. Overall, achieves data, providing valuable references management decision-making

Language: Английский

Citations

3
Hypoxia extreme events in a changing climate: Machine learning methods and deterministic simulations for future scenarios development in the Venice Lagoon DOI Creative Commons
Federica Zennaro, Elisa Furlan, Donata Melaku Canu

et al.

Marine Pollution Bulletin, Journal Year: 2024, Volume and Issue: 208, P. 117028 - 117028

Published: Oct. 3, 2024

Language: Английский

Citations

2
Key drivers of hypoxia revealed by time-series data in the coastal waters of Muping, China DOI
Xiangyang Zheng, Hui Liu, Qianguo Xing

et al.

Marine Environmental Research, Journal Year: 2024, Volume and Issue: 199, P. 106613 - 106613

Published: June 17, 2024

Language: Английский

Citations

1
Prediction of Total Phosphorus Concentration in Canals by GAT-Informer Model Based on Spatiotemporal Correlations DOI Open Access
Juan Huan, Xincheng Li,

Jialong Yuan

et al.

Water, Journal Year: 2024, Volume and Issue: 17(1), P. 12 - 12

Published: Dec. 24, 2024

The accurate prediction of total phosphorus (TP) is crucial for the early detection water quality eutrophication. However, predicting TP concentrations among canal sites challenging due to their complex spatiotemporal dependencies. To address this issue, study proposes a GAT-Informer method based on correlations predict in Beijing–Hangzhou Grand Canal Basin Changzhou City. begins by creating feature sequences each site time lag relationship concentration between sites. It then constructs graph data combining real river distance and correlation sequences. Next, spatial features are extracted fusing node using attention (GAT) module. employs Informer network, which uses sparse mechanism extract temporal efficiently simulating model was evaluated R2, MAE, RMSE, with experimental results yielding values 0.9619, 0.1489%, 0.1999%, respectively. exhibits enhanced robustness superior predictive accuracy comparison traditional models.

Language: Английский

Citations

1