A Hybrid Improved Dual-Channel and Dual-Attention Mechanism Model for Water Quality Prediction in Nearshore Aquaculture

Electronics, Journal Year: 2025, Volume and Issue: 14(2), P. 331 - 331

Published: Jan. 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.

Language: Английский

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Towards Explainable Artificial Intelligence for GNSS Multipath LSTM Training Models

Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 978 - 978

Published: Feb. 6, 2025

This paper addresses the critical challenge of understanding and interpreting deep learning models in Global Navigation Satellite System (GNSS) applications, specifically focusing on multipath effect detection analysis. As GNSS systems become increasingly reliant for signal processing, lack model interpretability poses significant risks safety-critical applications. We propose a novel approach combining Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells Layer-wise Relevance Propagation (LRP) to create an explainable framework detection. Our key contributions include: (1) development interpretable LSTM architecture processing observables, including variables, carrier-to-noise ratios, satellite elevation angles; (2) adaptation LRP technique analysis, enabling attribution decisions specific input features; (3) discovery correlation between relevance scores anomalies, leading new method anomaly Through systematic experimental validation, we demonstrate that our achieves high prediction accuracy across all parameters while maintaining interpretability. A finding emerges from controlled experiments: consistently increase during anomalous conditions, growth rates varying 7.34% 32.48% depending feature type. In validation experiments, systematically introduced anomalies time segments data sequence observed corresponding increases scores: showed 7.34–8.81%, ratios exhibited changes 12.50–32.48%, angle increased by 16.10%. These results potential LRP-based analysis enhancing quality monitoring integrity assessment. not only improves applications but also provides practical detecting analyzing contributing more reliable trustworthy navigation systems.

Language: Английский

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Research progress of non-destructive testing techniques in moisture content determination

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100878 - 100878

Published: March 1, 2025

Language: Английский

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Compound Hydrological Forecasting Model by Long Short-term Memory Network Coupled with Adaptive Mode Decomposition and Evolutionary Algorithm

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Language: Английский

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Integrated Runoff Forecasting Model with Mode Decomposition and Metaheuristic-optimized Bidirectional Gated Recurrent Unit

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

Language: Английский

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Twin extreme learning machine model and cooperation search algorithm for multi-step-ahead point and interval runoff prediction

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132778 - 132778

Published: Jan. 1, 2025

Language: Английский

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One Health and planetary health research landscapes in the Arab world

Science in One Health, Journal Year: 2025, Volume and Issue: 4, P. 100105 - 100105

Published: Jan. 1, 2025

This review explored research trends in One Health and planetary health the Arab world, a region confronting major sustainability challenges. These fields are crucial combating global pressing concerns like infectious diseases, biodiversity loss, antimicrobial resistance, climate change, air pollution. The COVID-19 pandemic stressed their significance to sustainable development. analysis assessed world's contributions these concepts applying performance visualization mapping, revealing that outperformed terms of productivity number contributed countries. Egypt, Saudi Arabia, United Emirates have emerged as leading contributors world. Meanwhile, States Kingdom, non-Arab nations, play pivotal role fostering collaborative efforts with region. trajectory has indeed shown remarkable exponential growth, especially since beginning 2019, which is an indication increasing relevance address Conversely, presents irregular growth pattern, strong point development this area standing out 2023. unique set social, cultural, governance, agricultural attributes joined by environmental challenges define focus both efforts. Climate contexts, public feature prominently health, focusing mainly on diseases addressing implications change human health. Advancing demands establishment regional governing body oversee integrated strategy, foster communities alliances, secure political will funding, ensure integration into policy academic frameworks.

Language: Английский

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Interpretable Hybrid Artificial Intelligence Model for Predicting Daily Hydropower Generation of Cascade Hydropower Reservoirs

Published: Jan. 1, 2024

Language: Английский

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