基于变压器-BiLSTM模型的空气质量指数的智能预测
Xinni Liu1,2, Kai Su3, Shubin Wang4
1School of Information, Xi'an University of Finance and Economics (XAUFE), No.360 Changning Street, Chang'an District, Xi'an, 710100, China. lxinni@xaufe.edu.cn.
Scientific reports
|November 25, 2025
概括
准确的空气质量预测对于公共卫生和环境管理至关重要. 结合变压器和BiLSTM网络的新混合深度学习模型显示,空气质量指数 (AQI) 的预测性能得到了改善.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 空气质量监测对于公共卫生,工业稳定和应对环境危害至关重要.
- 准确的空气质量指数 (AQI) 预测对于有效的环境管理和可持续发展至关重要.
研究的目的:
- 开发和评估混合深度学习模型,用于准确的AQI预测.
- 通过使用真实世界的空气质量数据,对比传统方法来评估模型的性能.
主要方法:
- 开发了一个混合深度学习模型,集成了一个变压器编码器和一个双向长短期存储器 (BiLSTM) 网络.
- 该模型在石家庄,北京和天津 (2013年11月至2025年2月) 的每日空气质量数据上进行了训练和验证.
主要成果:
- 变压器-BiLSTM模型展示了稳定可靠的AQI预测性能.
- 关键绩效指标包括RMSE为3.0012 ug/m3,MAE为1.7928 ug/m3,MAPE为3.3646%. 这两项指标均为3%.
- 与基线模型相比,混合模型表现出更高的准确性和概括性.
结论:
- 拟议的变压器-BiLSTM模型为AQI预测提供了一个可靠和可解释的工具.
- 这种方法为数据驱动的空气污染控制策略提供了定量支持.
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