计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 444-449.doi: 10.11896/JsJkx.190700158
丁子昂, 乐曹伟, 吴玲玲, 付明磊
DING Zi-ang, LE Cao-wei, WU Ling-ling and FU Ming-lei
摘要: PM2.5是衡量空气污染物浓度的核心指标。通过挖掘PM2.5历史数据的时序特性,完成对未来PM2.5浓度值的精确预测具有较强的学术意义和应用价值。然而,原始PM2.5浓度值时间序列数据相关性对模型的预测精度产生了较大的影响。为了解决这个问题,文中提出一种基于补充总体经验模态分解-皮尔逊相关分析(CEEMD-Pearson)和深度长短期记忆神经网络(Long Short Term Memory,LSTM)混合模型的PM2.5浓度预测方法。该方法利用补充总体经验模态分解(Complementary Ensemble Empirical Mode Decomposition,CEEMD)对PM2.5浓度历史数据进行不同频率的分解,增强数据中体现的时序特性。然后通过Pearson相关性检验方法对分解后的不同频率子波(IMFs)进行筛选,将筛选后的增强数据输入到多隐含层的深度LSTM网络的输入层进行训练并预测。实验数据表明,CEEMD-LSTM混合模型的预测精度为80%,但是该模型在训练次数为7000次左右才收敛;而经过Pearson二次筛选后的模型在训练800次左右就已经收敛,并且精度提升到87%;CEEMD-Pearson与深度LSTM神经网络混合模型的训练效果最优,在训练650次左右就已经收敛,并且预测精度达到了90%。实验结果说明,CEEMD模态分解方法可以展现出历史数据中的隐藏时序特性,结合Pearson相关性分析进行的二次筛选可有效地提升模型训练的收敛速度和预测精度。因此,基于CEEMD-Pearson和深度LSTM的混合模型可以获得最佳的训练效果、最快的收敛速度以及最精准的预测结果,可以有效解决PM2.5浓度预测问题。
中图分类号:
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