基于深度学习和支持向量机集成学习的PM2.5浓度24 h预测

提前24 h准确预测PM2.5浓度可以有效的避免严重污染天气对人体带来的不利影响.为了提高深度学习模型PM2.5浓度24 h预测的性能和泛化能力,在传统循环神经网络(RNN)模型上添加支持向量回归(SVR)作为下采样层提取非线性特征并降维;然后添加多核卷积神经网络(CNN)提升特征表达能力;最后利用门控循环网络(GRU...

PM2.5 24 hours prediction based on deep learning and support vector machine stacking model

Predicting PM2.5 24 hours in advance will avoid the harm from serious air pollution. To enhance the efficiency and generalization ability of deep learning prediction models for PM2.5 24 hours in advance prediction， a sub-sampled layer based on support vector regression (SVR) was attached to traditional recurrent Neural Network (RNN) to perform nonlinear features extraction and dimension reduction. And then a multi-kernel convolutional Neural Network (CNN) was employed to enhance feature expression. At last， a gated recurrent unit (GRU) network was employed to provide high stability of time sequence prediction using its ability in long time information memory. The air quality data and meteorological data of Wuhan and its surrounding 13 cities from January 1， 2015 to April 10， 2020 were employed to test the SVR-CNN-GRU. The experiment results showed that SVR-CNN-GRU exceed RNN， SVR and random forest methods in higher prediction accuracy and stronger generalization ability whose R2 is 0.97. The proposed method would provide high accuracy prediction for early warning 24 hours in advance.