[使用融合类堆叠算法预测杭州的臭氧度]
Hong-Zhao Dong1, Hong-Mei Guo1, Fang Ying2
1Joint Institute of Intelligent Transportation(Smart City), Zhejiang University of Technology, Hangzhou 310014, China.
Huan jing ke xue= Huanjing kexue
|September 26, 2024
概括
一种新的聚变堆叠算法 (FSOP) 通过将机器学习模型与普通最小平方 (OLS) 结合起来,提高了每天平均臭氧度的预测. 与单个模型相比,这种方法显著提高了预测准确性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 大气化学 大气化学
背景情况:
- 单个机器学习模型在准确预测每日平均臭氧度方面存在局限性.
- 准确的臭氧度预测对于环境监测和公共卫生至关重要.
研究的目的:
- 开发一种使用融合堆叠算法 (FSOP) 改进的臭氧度预测方法.
- 通过整合各种机器学习模型和统计方法来提高预测准确性.
主要方法:
- 利用了来自杭州 (2017-2022) 的每日最大8小时平均臭氧度和气象再分析数据.
- 使用LightGBM,LSTM和Informer算法开发了个别预测模型.
- 应用了与普通最小平方 (OLS) 的堆积算法来结合单个模型的预测.
主要成果:
- 该FSOP模型证明了更好的预测准确性和对臭氧度的适应效应.
- 获得了0.84的确定系数 (R2),19.65μg·m-3的RMSE和15.50μg·m-3.3的MAE.
- 与单个机器学习模型相比,FSOP模型的预测准确度提高了约8%.
结论:
- 聚变堆叠算法有效地整合了不同学习者的优势,以实现卓越的臭氧预测.
- FSOP为预测每日平均臭氧度提供了更准确,更可靠的方法.
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