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基于序列分解的混合神经网络对PM2.5度的短期预测

Xiaoxuan Wu^1,2,3, Jun Zhu^1,3, Qiang Wen^1,3

¹School of Artificial Intelligence and Big Data, Economic and Technological Development Zone, Hefei University, Hefei City, Anhui, China.

|May 10, 2024

概括

准确的PM2.5预测对于空气污染控制至关重要. 与其他深度学习方法相比,新的MIC-CEEMDAN-CNN-BiGRU模型显著提高了短期PM2.5预测的准确性.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
人工智能的人工智能

背景情况:

准确预测PM2.5度对于有效的空气污染减缓战略至关重要.
现有的预测模型经常与大气污染物的复杂,非线性动态作斗争.

研究的目的:

引入和评估一种新的混合深度学习模型,MIC-CEEMDAN-CNN-BiGRU,用于增强短期PM2.5度预测.
评估与各种已建立的深度学习框架相对应的拟议模型的预测性能.

主要方法:

使用最大信息系数 (MIC) 选择特征.
通过适应性噪声 (CEEMDAN) 进行完整集体实证模式分解的数据分解.
卷积神经网络 (CNN) 和双向循环门神经网络 (BiGRU) 的集成用于预测.

主要成果:

与RNN,LSTM,GRU和其他基于GRU的混合模型相比,MIC-CEEMDAN-CNN-BiGRU模型在PM2.5预测中表现出更高的准确性.
拟议的混合模型取得了显著的改进:R平方增加了近5%,平均绝对误差 (MAE) 减少了近5%,根平均平方误差 (RMSE) 减少了近11%.

结论:

新的MIC-CEEMDAN-CNN-BiGRU混合模型为短期PM2.5预测提供了更准确和更强大的方法.
这种先进的模型比现有方法提供了显著的改进,有助于更好地管理空气质量.

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