基于 VMD-DBO-LSTM 的空气质量预测

针对传统空气质量预测模型收敛速度慢，精度低的问题，提出一种基于变分模态分解(variational mode decom posi- tion,VMD) 和蜣螂优化算法(dung beetle optimizer,DBO)优化长短期记忆网络(long short term memory,LSTM)的预测模 型。首先，针对 AQI原始数据具有大量噪声的问题，使用VMD方法对非平稳信号进行模态分解以降低噪声对预测结果的影 响从而获得多个不同特征的模态分量；其次，针对 LSTM 靠人工经验调参存在一定局限性，利用DBO算法对LSTM 模型参数 进行优化；最后，对分解后的各个子序列使用LSTM 模型预测，将各个子序列进行叠加得到最后的预测结果。实验结果表明， VMD对非平稳数据的分解有助于提高预测精度，VMD-DBO-LSTM 模型的性能较其他模型均有不同程度的提高，该模型预 测的均方根误差为4.73μg/m³, 平均绝对误差为3.61μg/m³, 拟合度达到了97.8%。

Air quality predication based on VMD-DBO-LSTM

Aiming at the slow convergence speed and low accuracy of traditional air quality prediction models,a predication model based on variational mode decomposition(VMD)and dung beetle optimizer(DBO)was proposed to optimize long short term memory(LSTM).First of all,for the problem that the AQI raw data has a large amount of noise,the VMD method was used to decompose the nonstationary data to reduce the influence of noise on the prediction results,so as to obtain multiple modal components with different features.Secondly,there are some limitations in rely on manual parameter tuning based on human experience for LSTM,the DBO algorithm was used to optimize the LSTM model parameters.Finally,the LSTM model was used to predict each subseries after decomposition,and the subseries are superimposed to obtain the final prediction result.The experimental results show that the decomposition of nonstationary data by VMD can help improve the prediction accuracy,and the performance of VMD-DBO-LSTM model is improved to varying degrees compared with other models,the root mean square error of this model prediction is4.73μg/m,the average absolute error is 3.61 μg/m³,the goodness of fit reach 97.8%.