一种基于深度学习的当日PM_(2.5)混合集成预测方法

An enhanced hybrid ensemble deep learning approach for forecasting daily PM_(2.5)

PM_(2.5)预测技术可为环境治理和保护公众健康提供科学依据。为预测PM_(2.5),本文提出一种新的混合集成深度学习模型。整个模型可以描述为:利用变分模态分解(VMD)将原始PM_(2.5)序列分解为8个不同频率特性的子序列,采用长短期记忆网络(LSTM)、回声状态网络(ESN)和时间卷积网络(TCN)对8个不同频率PM_(2.5)子序列进行并行预测,采用梯度增强决策树(GBDT),对LSTM、ESN和TCN的预测结果进行集成重构。基于采集于沈阳、长沙和深圳3个城市的PM_(2.5)数据进行实验,得出以下结论:相对于传统的启发式集成方法,GBDT是一种更有效的集成优化方法。本文所提出模型在3个实验数据集上的MAE分别为1.587、1.718和1.327μg/m^(3),相对于其他16个对比模型,本文所提出预测模型具有更优秀的预测性能。

PM_(2.5) forecasting technology can provide a scientific and effective way to assist environmental governance and protect public health.To forecast PM_(2.5),an enhanced hybrid ensemble deep learning model is proposed in this research.The whole framework of the proposed model can be generalized as follows:the original PM_(2.5) series is decomposed into 8 sub-series with different frequency characteristics by variational mode decomposition(VMD);the long short-term memory(LSTM)network,echo state network(ESN),and temporal convolutional network(TCN)are applied for parallel forecasting for 8 different frequency PM_(2.5) sub-series;the gradient boosting decision tree(GBDT)is applied to assemble and reconstruct the forecasting results of LSTM,ESN and TCN.By comparing the forecasting data of the models over 3 PM_(2.5) series collected from Shenyang,Changsha and Shenzhen,the conclusions can be drawn that GBDT is a more effective method to integrate the forecasting result than traditional heuristic algorithms;MAE values of the proposed model on 3 PM_(2.5) series are 1.587,1.718 and 1.327μg/m3,respectively and the proposed model achieves more accurate results for all experiments than sixteen alternative forecasting models which contain three state-of-the-art models.