Abstract
Long-term prediction of hour-concentration of PM2.5 (particles in atmospheric suspension with effective dimensions equal or lower than 2.5 microns) is of great significance for environmental protection and people’s health. At present, the prediction of hour-concentration of PM2.5 is mostly single-step prediction, which is to predict PM2.5 concentration at a future time point based on a period of historical data. In this paper, a model based on multi-time scale fusion is proposed to study single-step prediction and multi-step prediction, respectively. Experimental results show that the proposed model is better than stacked LSTM and CNN-LSTM in predicting PM2.5 hour-concentration.
Highlights
PM2.5 data is affected by a variety of related time series, but the change of each time series value does not immediately affect PM2.5 concentration value, which means that the variable value at the previous moment has a lag effect on the PM2.5 concentration value at the moment, which may be strong in the short term and weak in the long term [8]
Because the characteristic information of different time is proposed in this paper
The experimental results show that the proposed multi-time scales has different onis the prediction results,model a multi-time scale fusionprediction, model is scaleinfluence fusion model superior to the comparison in single and multi-step proposed in thisindicating paper
Summary
Convolutional Network and Long Short-Term Memory), which proved that the model was superior to CNN (Convolutional Neural Networks) and LSTM in predicting the air quality in the future one hour [2]. Huang and Kuo constructed the model APNet (Attention-based Parallel Networks) and proved through experiments that the model was superior to CNN and LSTM in predicting PM2.5 concentration in the future one hour [4]. 2. PM2.5 Prediction Model Based on Multi-Time Scale Fusion structures, include, the input gate, the forget gate, and the output gate. The directions are: Input ht−1 and xt into the forget gate, and calculate the output value ft of the forget gate the sigmoid activation function.
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