基于深度学习混合框架的空气质量预测模型
Chao Yin1, Weidong Li1, Tongfang Li1
1School of Computer and Big Data Science, Jiujiang University, Jiujiang, 332005, People's Republic of China.
Scientific reports
|February 2, 2026
概括
本研究介绍了CBLA模型,用于准确的城市空气质量预测. 混合模型结合了深度学习和机器学习来预测PM2.5度,帮助污染控制工作.
科学领域:
- 环境科学与工程环境科学与工程
- 环境监测中的人工智能
- 大气科学和空气质量管理
背景情况:
- 加快的工业化和现代化加剧了全球空气污染问题.
- 准确的空气质量预测对于有效的污染预防和控制战略至关重要.
- 现有的模型可能缺乏复杂的城市大气动态所需的精度.
研究的目的:
- 开发一种新的混合模型,以提高城市空气质量预测.
- 提高预测PM2.5度的准确性和可靠性.
- 为空气污染管理提供强有力的技术支持.
主要方法:
- 一个混合模型 (CBLA) 集成一维卷积神经网络 (1D-CNNs),双向长短期记忆 (BiLSTM) 网络和注意力机制.
- 1D-CNNs用于从空气质量数据中提取深度特征.
- BiLSTM用于时间序列分析,注意力机制用于特征优化,以及XGBoosting用于将预测与气象数据集成.
主要成果:
- 在空气质量预测任务中,CBLA模型表现出色.
- 使用北京数据集进行的实验评估证实了该模型的有效性.
- 混合方法成功地捕获了复杂的时间依赖性和影响因素.
结论:
- 拟议的CBLA模型为准确的城市空气质量预测提供了一个强大的工具.
- 整合CNN,BiLSTM,注意力和XGBoosting显著提高了预测的准确性.
- 这种方法为空气污染控制和环境保护工作做出了宝贵的贡献.
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