基于EMD-变压器-BiLSTM的短期空气质量预测
Jie Dong1,2, Yaoli Zhang2, Jiang Hu3
1Fudan University, Shanghai, 200433, China.
Scientific reports
|September 3, 2024
概括
本研究引入了一种用于准确空气质量预测的新型混合模型,将实证模式分解 (EMD) 与变压器和双向长短期存储器 (BiLSTM) 网络相结合,以高精度预测空气质量指数 (AQI).
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 空气质量时间序列数据是挥发性的和非静止的,这对准确的预测构成了挑战.
- 现有的方法难以应对含有噪声的非线性时间序列数据的复杂性.
研究的目的:
- 开发和验证一种混合模型,用于准确的超短期空气质量预测.
- 为了应对预测非线性,杂的空气质量时间序列数据的挑战.
主要方法:
- 经验模式分解 (EMD) 将AQI分解为内在模式函数 (IMF).
- 基于双向长短期内存 (BiLSTM) 的改进的变压器算法用于预测IMF.
- 使用BiLSTM整合预测的IMF,以获得最终的AQI预测.
主要成果:
- 混合模型在印度帕特纳的5小时AQI预测中实现了低误差指标 (RMSE: 5.6853,MAE: 2.8230,MAPE: 2.23%).
- 该模型展示了来自多个城市的空气质量数据集的可扩展性和高性能.
结论:
- 拟议的EMD-变压器-BiLSTM混合模型为实时空气质量预测提供了强大的解决方案.
- 该模型显示了在环境监测和公共卫生领域广泛应用的巨大潜力.
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