Improvement and application of multi-layer LSTM algorithm based on spatial-temporal correlation

Abstract

Current algorithms for the prediction of air pollutant particle concentration generally failed to effectively integrate with the time dependence and spatial correlation features of particle concentration. To this end, this paper studied the improvement and application of the multilayer LSTM algorithm based on spatial-temporal correlation. First, the paper proposed the method for calculating the correlation coefficients of air pollutant particle concentration in global and local regions, and established the matrix for the corresponding correlation coefficients; then layer by layer, the K-1 layer LSTM algorithm was used to extract the time dependence eigen vector H of the particle concentration at N observation sites, and calculate the product (R) of the local correlation coefficient matrix and eigen vector H, so as to achieve the fusion of time dependence and spatial correlation features in local region; at last, at the K layer, the inner product of the global correlation coefficient matrix and R was calculated to extract the spatial correlation feature of particle concentration in global and local regions. On the global and local datasets, the proposed algorithm was compared with the LSTME algorithm, space-time deep learning (STDL) algorithm, time delay neural network (TDNN) algorithm, autoregressive moving average (ARMA) algorithm, support vector regression (SVR) algorithm and the traditional LSTM NN algorithm. The comparison results showed that, in terms of air particle concentration prediction, the proposed algorithm outperformed the other algorithms, proving that the multilayer neural network based on spatial-temporal correlation can effectively improve the prediction performance of the LSTM algorithm.