通过综合深度学习框架,提高空气质量指数的预测
Sudha Raja1, Ajith Damodaran2, Gunaselvi Manohar3
1Department of Mechanical Engineeeing, Easwari Engineeeing College, Chennai - 600 089, Tamil Nadu, India. sudha.r@eec.srmrmp.edu.in.
Environmental science and pollution research international
|December 12, 2025
概括
这项研究介绍了一种混合深度学习模型,将LSTM,CNN和GNN结合起来,用于优越的空气质量预测. 这种先进的模型显著提高了公共卫生和环境政策的预测准确性和稳定性.
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:
- 准确的空气质量预测对于公共卫生和政策至关重要.
- 传统模型与复杂的空气质量数据依赖性作斗争.
研究的目的:
- 开发一种混合深度学习模型,用于增强空气质量预测.
- 整合多个神经网络架构和组合方法,以提高准确性.
主要方法:
- 混合深度学习模型集成长期短期记忆 (LSTM),卷积神经网络 (CNN) 和图形神经网络 (GNN).
- 组合学习技术 (堆叠和提升) 用于融合模型输出.
- 使用真实世界的空气质量监测数据集进行评估.
主要成果:
- 与基线模型相比,实现了超过0.92的R2得分,并减少了预测错误.
- 在北京数据集 (MAE 11.30,R2 0.89) 与独立模型相比,表现出优异的性能.
- 通过交叉数据集评估 (北京到洛杉矶) 展示了可靠性和可转移性.
结论:
- 混合模型在空气质量预测准确性和一致性方面提供了显著的改进.
- 拟议的框架对环境监测具有很强的概括性和解释性.
- 这种方法为污染敏感地区的决策提供了有希望的工具.
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