基于VMD-SSA-BILSTM的空气质量预测模型
Air Quality Prediction Based on VMD-SSA-BILTSM Model
DOI: 10.12677/mos.2024.136526, PDF,    国家自然科学基金支持
作者: 陈榴娜, 刘媛华：上海理工大学管理学院，上海
关键词: 空气质量预测；麻雀搜索算法；变分模态分解；双向长短期记忆神经网络；组合模型；Air Quality Prediction； SSA； VMD； BILSTM； Combined Model
摘要: 随着城市化现代化的发展，空气污染日益严重。近年来，为了提高空气质量预测的准确度，空气质量预测模型层出不穷。本文提出了VMD-SSA-BILSTM组合模型来预测空气质量指数。首先，利用变分模态分解(VMD)方法将不稳定的空气质量时间序列数据分解成不同的模态。然后，利用麻雀搜索算法(SSA)对双向长短期记忆神经网络(BILSTM)模型的参数进行寻优，进而输出空气质量预测的结果。最后，利用上海市的空气质量相关数据对模型进行验证。结果表明，VMD-SSA-BILSTM模型比单一的BILSTM模型和VMD-BILSTM模型具有更小的误差，提高了空气质量预测的准确性，精度提升显著，具有良好的应用前景。
Abstract: With the development of urbanization modernization, air pollution is becoming more and more serious. In recent years, in order to improve the accuracy of air quality prediction, air quality prediction models have emerged in an endless stream. This paper proposes a VMD-SSA-BILSTM combined model to predict air quality index. Firstly, the VMD method is used to decompose the unstable time series data of air quality into different modes. Then, the sparrow search algorithm is used to optimize the parameters of the model, and then the results of air quality prediction are output. Finally, the air quality related data of Shanghai are used to verify this model. The results show that the VMD-SSA-BILSTM model has smaller errors than the single BILSTM model and the VMD-BILSTM model, and improves the accuracy of air quality prediction. The accuracy improvement is significant and has good application prospects.
文章引用：陈榴娜, 刘媛华. 基于VMD-SSA-BILSTM的空气质量预测模型[J]. 建模与仿真, 2024, 13(6): 5781-5790. https://doi.org/10.12677/mos.2024.136526

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