监控和预测PM2.5浓度变化对人类健康和环境污染治理至关重要。本文旨在研究PM2.5浓度长期预测任务中精度较低的问题。通过融合空间特征提取、空间注意力机制增强以及长时间序列特征提取,提出了一种预测模型,能够精准捕捉长序列中PM2.5...监控和预测PM2.5浓度变化对人类健康和环境污染治理至关重要。本文旨在研究PM2.5浓度长期预测任务中精度较低的问题。通过融合空间特征提取、空间注意力机制增强以及长时间序列特征提取,提出了一种预测模型,能够精准捕捉长序列中PM2.5浓度变化趋势。该模型首先通过CNN提取空间特征,并利用空间注意力机制强化关键空间信息。然后,由XLSTM捕捉时间序列中的动态变化和长期依赖关系。本章模型在两个大城市的数据集上进行了实验,并与FXX、LSTM、XLSTM以及CNN-XLSTM进行了对比分析。结果表明,本文模型在所有评估指标上均优于对比模型,充分验证了其有效性和泛化能力。Monitoring and predicting changes in PM2.5 concentration is crucial for human health and environmental pollution control. This paper aims to investigate the issue of low accuracy in long-term PM2.5 concentration prediction tasks. By integrating spatial feature extraction, spatial attention mechanism enhancement, and long-term sequence feature extraction, a predictive model is proposed that is capable of accurately capturing the trends of PM2.5 concentration variations over extended sequences. Specifically, the model first extracts spatial features using CNN and enhances key spatial information through a spatial attention mechanism. Subsequently, XLSTM captures dynamic changes and long-term dependencies within the time series. The model is evaluated on datasets from two major cities. The results show that the proposed model outperforms comparison models, including FNN, LSTM, XLSTM, and CNN-XLSTM, across all evaluation metrics, fully validating its effectiveness and generalization capability.展开更多
文摘监控和预测PM2.5浓度变化对人类健康和环境污染治理至关重要。本文旨在研究PM2.5浓度长期预测任务中精度较低的问题。通过融合空间特征提取、空间注意力机制增强以及长时间序列特征提取,提出了一种预测模型,能够精准捕捉长序列中PM2.5浓度变化趋势。该模型首先通过CNN提取空间特征,并利用空间注意力机制强化关键空间信息。然后,由XLSTM捕捉时间序列中的动态变化和长期依赖关系。本章模型在两个大城市的数据集上进行了实验,并与FXX、LSTM、XLSTM以及CNN-XLSTM进行了对比分析。结果表明,本文模型在所有评估指标上均优于对比模型,充分验证了其有效性和泛化能力。Monitoring and predicting changes in PM2.5 concentration is crucial for human health and environmental pollution control. This paper aims to investigate the issue of low accuracy in long-term PM2.5 concentration prediction tasks. By integrating spatial feature extraction, spatial attention mechanism enhancement, and long-term sequence feature extraction, a predictive model is proposed that is capable of accurately capturing the trends of PM2.5 concentration variations over extended sequences. Specifically, the model first extracts spatial features using CNN and enhances key spatial information through a spatial attention mechanism. Subsequently, XLSTM captures dynamic changes and long-term dependencies within the time series. The model is evaluated on datasets from two major cities. The results show that the proposed model outperforms comparison models, including FNN, LSTM, XLSTM, and CNN-XLSTM, across all evaluation metrics, fully validating its effectiveness and generalization capability.
