Multi-scale spatial pyramid attention mechanism for image recognition: An effective approach

Yu, Yang; Zhang, Yi; Cheng, Zeyu; Song, Zhe; Tang, Chengkai

doi:10.1016/j.engappai.2024.108261

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…When ResNet is combined with a TCN, a more comprehensive feature representation and modelling capability can be created, enhancing the model's ability to represent complex patterns and improving prediction accuracy. In addition, a sparse attention (SA) mechanism was specifically incorporated in this study to take advantage of the sparsity that it introduces to strengthen the model's focus on key features in time-series data [20]. This strategy greatly improves the sensitivity of the model to key temporal nodes and features in the prediction of pollutant dispersion peaks, providing strong support for accurate prediction.…”

Section: Introductionmentioning

confidence: 99%

Advancing Spatiotemporal Pollutant Dispersion Forecasting with an Integrated Deep Learning Framework for Crucial Information Capture

Wang,

Luo,

Kong

et al. 2024

Sustainability

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This study addressed the limitations of traditional methods in predicting air pollution dispersion, which include restrictions in handling spatiotemporal dynamics, unbalanced feature importance, and data scarcity. To overcome these challenges, this research introduces a novel deep learning-based model, SAResNet-TCN, which integrates the strengths of a Residual Neural Network (ResNet) and a Temporal Convolutional Network (TCN). This fusion is designed to effectively capture the spatiotemporal characteristics and temporal correlations within pollutant dispersion data. The incorporation of a sparse attention (SA) mechanism further refines the model’s focus on critical information, thereby improving efficiency. Furthermore, this study employed a Time-Series Generative Adversarial Network (TimeGAN) to augment the dataset, thereby improving the generalisability of the model. In rigorous ablation and comparison experiments, the SAResNet-TCN model demonstrated significant advances in predicting pollutant dispersion patterns, including accurate predictions of concentration peaks and trends. These results were enhanced by a global sensitivity analysis (GSA) and an additive-by-addition approach, which identified the optimal combination of input variables for different scenarios by examining their impact on the model’s performance. This study also included visual representations of the maximum downwind hazardous distance (MDH-distance) for pollutants, validated against the Prairie Grass Project Release 31, with the Protective Action Criteria (PAC) and Immediately Dangerous to Life or Health (IDLH) levels serving as hazard thresholds. This comprehensive approach to contaminant dispersion prediction aims to provide an innovative and practical solution for environmental hazard prediction and management.

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Section: Introductionmentioning

confidence: 99%