Application Research Of Spatio-temporal Sequence Prediction In Air Quality Data

In recent years,air pollution has seriously affected people's production and life.Therefore,the analysis and prediction of air quality data has become a research hotspot in recent years.When analyzing this type of air quality data,it is found that this type of data is not only time-correlated but also spatially correlated.Such data has multi-source,multi-variable,multi-scale temporal and spatial characteristics.This thesis conducts spatio-temporal analysis on air quality data with spatio-temporal features,and uses the model proposed in this paper to predict,which further improves the prediction accuracy of air treatment data,and provides data support for the prevention and treatment of air pollution problems.Aiming at the computational process complexity and ill-conditioned matrix problems of the traditional space-time Kriging interpolation model,this paper combines the elastic network algorithm with the spatiotemporal Kriging model,and solves the ill-conditioned matrix solution using the elastic network algorithm to improve the robustness of the understanding.Compared with the traditional space-time Kriging interpolation model,the improved spatiotemporal Kriging interpolation model can effectively ensure the robustness of the understanding and improve the interpolation precision.At the same time,this paper uses air quality data to verify.In addition,in order to verify the model's good generalization performance,this paper uses the temperature dataset of the Yangtze River Delta region to verify,and also obtains good prediction results.Aiming at the inevitable precision or computational complexity of a single predictive model,this paper proposes a hybrid spatiotemporal prediction model.The model mainly uses the gray correlation analysis method to quantify the spatial relationship,and uses the spatial relationship to construct the spatial weight matrix.The spatial weight matrix can retain the effective space information in the spatiotemporal data.Spatio-temporal feature extraction is performed by non-negative sparse self-encoding to ensure effective spatio-temporal information to the greatest extent and improve prediction accuracy.The model uses an echo state neural network as a predictive framework.From the perspective of spatial relationship capture,the hybrid model proposes a new spatial weight calculation method to effectively preserve spatial information.At the same time,the echo state network is combined as a prediction model,and the hybrid model is applied to the SO₂ concentration data set and the air quality data set in the air.