DF-OSELM:一个动态反功能学习模型,用于在线空气质量预测
Yujie Liu1, Fadratul Hafinaz Hassan2, Li-Pei Wong1
1School of Computer Sciences, Universiti Sains Malaysia, 11800, Gelugor, Pulau Pinang, Malaysia.
Environmental monitoring and assessment
|October 31, 2025
概括
这项研究引入了一个新的动态反功能学习在线序列极端学习机器 (DF-OSELM),用于准确的实时空气质量预测. 该模型显著提高了PM2.5.5等污染物的预测性能和效率.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 准确的空气质量预测对于公共卫生和污染风险减轻至关重要.
- 现有的模型在适应性,计算速度和解释性方面扎.
研究的目的:
- 开发一个先进的在线顺序极端学习机器,用于实时空气质量预测.
- 提高模型的适应性,效率和可解释性.
主要方法:
- 提出了一个动态反功能学习在线顺序极端学习机器 (DF-OSELM).
- 集成的双重极端学习机器自动编码器 (ELM-AEs),一个规范化层和一个反复反机制.
- 在线训练模型使用每小时10,000个空气质量样本 (PM2,PM10,SO2,NO2).
主要成果:
- DF-OSELM实现了卓越的预测性能 (NRMSE < 0.1,R2 > 0.99),表现优于基线模型.
- 废除研究证实了正常化和双自编码机制的重要性.
- 不确定性量化提供了可靠的置信区间,SHAP分析确定了关键预测因素.
结论:
- DF-OSELM为实时空气质量监测提供了精度,效率 (更新时间<3ms) 和可解释性的平衡方法.
- 该模型适用于大型环境平台和风险评估.
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