基于时间序列的空气质量历史相关模型
Ying Liu1, Lixia Wen2,3, Zhengjiang Lin4
1School of Enviromental Science and Engineering, Southwest Jiaotong University, Chengdu, 611756, China.
Scientific reports
|October 1, 2024
概括
使用高斯隐马尔科夫模型 (GHMM) 改进了准确的空气质量预测,该模型分析了时间特征和污染物排放. 结合历史和气象模型可以提高预测的稳定性和准确性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 大气科学 大气科学
背景情况:
- 准确的空气质量预测对人类健康和社会发展至关重要.
- 现有的模型往往忽略了时间动态和排放污染物关系.
- 空气质量指数 (AQI) 是具有固有的时间特征的时间序列.
研究的目的:
- 开发一个改进的空气质量预测模型,使用历史数据和污染物排放.
- 为了更有效地利用AQI数据的时间特征.
- 与传统方法相比,提高预测准确性和稳定性.
主要方法:
- 利用高斯隐马尔科夫模型 (GHMM) 进行时间序列分析.
- 采用穿越方法在GHMM中选择最佳隐藏状态.
- 应用多天权重匹配和固定训练集长度用于GHMM优化.
- 实施了用于AQI预测的直接和间接预测模式.
- 集成的历史相关模型与先前的气象相关模型.
主要成果:
- 使用间接预测模式的优化GHMM显示了更好的准确性 (MAE=13.59,RMSE=17.59).
- 整合历史和气象相关模型进一步提高了预测准确性 (MAE=11.59,RMSE=14.87).
- GHMM展示了强大的时间特征分析能力,提高了预测稳定性.
结论:
- 高斯隐马尔科夫模型通过捕捉时间动态显著提高了空气质量预测的准确性和稳定性.
- 将历史排放数据与气象因素相结合,可以获得更强大,更准确的空气质量预测.
- 这种综合方法为了解和预测空气质量提供了更全面的方法.
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