通过使用包装和LSTM神经网络,预测PM2.5度,提高状态趋势意识和不确定性分析
Chao Bian1,2, Guangqiu Huang1
1School of Management, Xi'an University of Architecture and Technology, Xi'an, China.
Journal of environmental quality
|June 20, 2024
概括
这项研究提高了PM2.5空气污染物预测,使用状态趋势意识和一种新的LSTM-bagging模型. 该方法提高了预测准确性,并为更好的环境监测和公共卫生决策提供了概率范围.
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:
- 全球环境保护工作越来越多地集中在监测细颗粒物 (PM2.5).
- 准确预测PM2.5度对于环境管理和公共卫生至关重要.
- 传统的方法经常与空气污染数据的动态性和复杂性作斗争.
研究的目的:
- 将状态趋势意识概念从大数据分析应用到PM2.5预测.
- 开发一种先进的模型,以便更准确,更可靠地预测PM2.5度.
- 将间隔预测纳入用于量化预测不确定性.
主要方法:
- 利用状态趋势意识进行整体数据洞察和决策支持.
- 开发了一种混合模型,将长短期记忆 (LSTM) 神经网络与包装合集算法结合起来.
- 实现间隔预测,以预测PM2.5变化的概率范围.
主要成果:
- 拟议的LSTM-bagging模型在准确性和概括性方面明显优于传统的LSTM和支持矢量机 (SVM) 方法.
- 与SVM-LSTM模型相比,实现了12%的错误减少.
- 证明了预测准确度的提高,增强了模型的概括性,并减少了过拟合.
结论:
- 开发的现状趋势意识的LSTM后退模型与间隔预测提供了一个强大的环境监测工具.
- 这种方法提高了关于公共卫生和环境保护的决策能力.
- 该方法有效地解决了预测不确定性的问题,为PM2.5变化提供了概率范围.
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