[基于SARIMA-BP神经网络的城市近地表面臭氧预测模型]
Cheng-Li Xu1, Chao-Yang Zheng1
1Department of Mathematics and Statistics, Hefei University, Hefei 230601, China.
Huan jing ke xue= Huanjing kexue
|March 14, 2026
概括
一个新的混合机器学习模型结合季节性自回归整体滑动平均 (SARIMA) 和逆向传播神经网络 (BPNN) 显著改善了城市臭氧污染的预测. 这种综合方法提高了有效环境管理的预测准确性.
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:
- 城市臭氧 (O3) 污染在中国因快速城市化和工业化而越来越令人担忧.
- 传统的时间序列模型往往无法在O3度预测中考虑随机因素.
- 准确的O3预测对于制定有效的污染控制策略至关重要.
研究的目的:
- 提出一种新的机器学习融合模型,用于改进城市O3度预测.
- 解决传统模型在捕捉 O3 污染的随机元素方面的局限性.
- 提高O3污染预测的准确性,以改善环境管理.
主要方法:
- 开发了一个综合模型,将季节性自回归整体滑动平均 (SARIMA) 和逆向传播神经网络 (BPNN) 结合起来.
- 使用季节性趋势分解 (STL) 将O3数据分成线性 (趋势,季节性) 和非线性 (随机) 组件.
- 应用SARIMA用于线性元件预测和BPNN用于非线性元件装配,将两者整合为最终输出.
主要成果:
- 与单个SARIMA和BP模型相比,集成的SARIMA-BP模型显示出更高的预测准确性.
- 在O3度预测中达到8.3852μg·m−3的根平均平方误差 (RMSE).
- 超越了SARIMA-长期短期记忆 (LSTM) 模型的表现,表明了增强的预测能力.
结论:
- SARIMA和BPNN的融合为预测城市O3污染提供了一个强大而准确的方法.
- 该模型能够处理线性和非线性模式,这大大提高了预测准确度.
- 为城市臭氧污染预防和控制举措提供了宝贵的理论基础.
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