Research on a prediction method for carbon dioxide concentration based on an optimized lstm network of spatio-temporal data fusion

In view of the different spatial and temporal resolutions of observed multi-source heterogeneous carbon dioxide data and the uncertain quality of observations, a data fusion prediction model for observed multiscale carbon dioxide concentration data is studied. First, a wireless carbon sensor network is created, the gross error data in the original dataset are eliminated, and remaining valid data are combined with kriging method to generate a series of continuous surfaces for expressing specific features and providing unified spatio-temporally normalized data for subsequent prediction models. Then, the long short-term memory network is used to process these continuous time- and space-normalized data to obtain the carbon dioxide concentration prediction model at any scales. Finally, the experimental results illustrate that the proposed method with spatio-temporal features is more accurate than the single sensor monitoring method without spatio-temporal features.