Spatial-temporal correlation-based LSTM algorithm and its application in PM2.5 prediction

Abstract

In existing researches, the algorithms for simulating and predicting the evolution process of air pollutant particle concentration have neither explored the spatial correlation of particle concentration in depth, nor achieved the fusion of the time dependence and the spatial correlation of the particle concentration. To this end, this paper proposes the long-short term memory network (LSTM) algorithm based on spatiotemporal fusion. First, the spatial correlation, the relevant factors and the calculation methods are proposed; then, the local spatial correlation factors are combined with the forget gate and the remember gate of the LSTM algorithm to construct a LSTM algorithm based on local spatial information and spatial-temporal correlation, namely the LTS-LSTM; after that, the learning result of LTS-LSTM is combined with the global spatial correlation factors to construct a LSTM algorithm based on global spatial information and spatial-temporal correlation, namely the GTS-LSTM; at last, the proposed algorithm is adopted to simulate the global and local air pollution particle concentration evolution process, and predict the particle concentration. On the global and local observation dataset, the proposed algorithm is compared with the regression algorithm, support vector machine (SVM), fuzzy neural network (FNN), LSTM neural network, GC-LSTM neural network, and DL-LSTM neural network. The comparison results show that: in terms of air particle concentration prediction, the performance of the proposed algorithm outperforms other traditional prediction algorithms, and its performance is close to the deep LSTM algorithms.