Environmental Research, Год журнала: 2024, Номер 267, С. 120683 - 120683
Опубликована: Дек. 20, 2024
Язык: Английский
Environmental Research, Год журнала: 2024, Номер 267, С. 120683 - 120683
Опубликована: Дек. 20, 2024
Язык: Английский
Electronics, Год журнала: 2025, Номер 14(2), С. 331 - 331
Опубликована: Янв. 15, 2025
The aquatic environment in aquaculture serves as the foundation for survival and growth of animals, while a high-quality water is necessary condition promoting efficient healthy development. To effectively guide early warnings regulation quality aquaculture, this study proposes predictive model based on dual-channel dual-attention mechanism, namely, DAM-ResNet-LSTM model. This encompasses two parallel feature extraction channels: residual network (ResNet) long short-term memory (LSTM), with mechanisms integrated into each channel to enhance model’s representation capabilities. Then, proposed trained, validated, tested using meteorological parameter data collected by an offshore farm environmental monitoring system. results demonstrate that structure mechanism can significantly improve performance prediction accuracy pH, dissolved oxygen (DO), salinity (SAL) (with Nash coefficients 0.9361, 0.9396, 0.9342, respectively) higher than chemical demand (COD), ammonia nitrogen (NH3-N), nitrite (NO2−), active phosphate (AP) 0.8578, 0.8542, 0.8372, 0.8294, respectively). Compared single-channel DA-ResNet (ResNet mechanism), predicting DO, SAL, COD, NH3-N, NO2−, AP increase 12.76%, 12.58%, 11.68%, 18.350%, 19.32%, 16%, 14.99%, respectively. DA-LSTM (LSTM corresponding increases are 9.15%, 9.93%, 9.11%, 10.91%, 10.11%, 10.39%, 10.2%, ResNet-LSTM LSTM parallel) without attention improvements 1.91%, 2.4%, 0.74%, 3.41%, 2.71%, 3.55%, 4.13%, fulfills practical requirements accurate forecasting nearshore aquaculture.
Язык: Английский
Процитировано
1Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103126 - 103126
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
1Water Resources Management, Год журнала: 2025, Номер unknown
Опубликована: Фев. 8, 2025
Язык: Английский
Процитировано
0Water, Год журнала: 2025, Номер 17(8), С. 1131 - 1131
Опубликована: Апрель 10, 2025
Excessive total nitrogen (TN) in water bodies leads to eutrophication, algal blooms, and hypoxia, which pose significant risks aquatic ecosystems human health. Accurate real-time TN prediction is crucial for effective quality management. This study presents an innovative approach that combines the distance correlation coefficient (DCC) feature selection with a coupled Attention-Convolutional Neural Network-Bidirectional Long Short-Term Memory (At-CBiLSTM) model predict concentrations Dongjiang River China. A dataset of 28,922 time-series data points was collected from seven sampling sites along River, spanning November 2020 February 2023. The DCC method identified conductivity, Permanganate Index (CODMn), phosphorus as most predictors levels. At-CBiLSTM model, optimized time step three, outperformed other models, including standalone (LSTM), Bi-directional LSTM (Bi-LSTM), Convolutional Network (CNN-LSTM), Attention-LSTM variants, achieving excellent performance following metrics: mean absolute error (MAE) = 0.032, squared (MSE) 0.005, percentage (MAPE) 0.218, root (RMSE) 0.045. Importantly, increasing number input features beyond three variables led decline accuracy, underscoring importance DCC-driven selection. results highlight combining deep learning particularly At-CBiLSTM, effectively captures nonlinear temporal dependencies improves accuracy. provides solid foundation monitoring can inform targeted pollution control strategies river ecosystems.
Язык: Английский
Процитировано
0Intelligent Systems with Applications, Год журнала: 2025, Номер unknown, С. 200523 - 200523
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133424 - 133424
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0Materials Today Communications, Год журнала: 2024, Номер unknown, С. 111137 - 111137
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
2Environmental Research, Год журнала: 2024, Номер 267, С. 120683 - 120683
Опубликована: Дек. 20, 2024
Язык: Английский
Процитировано
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