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

и другие.

Stochastic Environmental Research and Risk Assessment, Год журнала: 2024, Номер unknown

Опубликована: Сен. 28, 2024

Язык: 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

и другие.

Marine Environmental Research, Год журнала: 2025, Номер 209, С. 107170 - 107170

Опубликована: Апрель 24, 2025

Язык: Английский

Процитировано

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

и другие.

Ecological Informatics, Год журнала: 2024, Номер 85, С. 102944 - 102944

Опубликована: Дек. 9, 2024

Язык: Английский

Процитировано

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

и другие.

Environmental Modelling & Software, Год журнала: 2025, Номер 188, С. 106412 - 106412

Опубликована: Март 5, 2025

Язык: Английский

Процитировано

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

Lin Ye,

Minquan Guo

и другие.

Ocean & Coastal Management, Год журнала: 2025, Номер 265, С. 107628 - 107628

Опубликована: Март 22, 2025

Язык: Английский

Процитировано

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

и другие.

Journal of Environmental Management, Год журнала: 2025, Номер 382, С. 125406 - 125406

Опубликована: Апрель 18, 2025

Язык: Английский

Процитировано

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

F. Liu

и другие.

Lecture notes in civil engineering, Год журнала: 2025, Номер unknown, С. 361 - 376

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

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

Jingzhe Hu,

Peixuan Wang,

Dashe Li

и другие.

Ecological Informatics, Год журнала: 2024, Номер 82, С. 102695 - 102695

Опубликована: Июнь 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

Язык: Английский

Процитировано

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

и другие.

Marine Pollution Bulletin, Год журнала: 2024, Номер 208, С. 117028 - 117028

Опубликована: Окт. 3, 2024

Язык: Английский

Процитировано

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

и другие.

Marine Environmental Research, Год журнала: 2024, Номер 199, С. 106613 - 106613

Опубликована: Июнь 17, 2024

Язык: Английский

Процитировано

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

и другие.

Water, Год журнала: 2024, Номер 17(1), С. 12 - 12

Опубликована: Дек. 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.

Язык: Английский

Процитировано

1