Atmospheric pollution factor concentration space-time distribution prediction method and system

Abstract

The invention relates to an atmospheric pollution factor concentration space-time distribution prediction method and system, and the method comprises the following steps: constructing a sparse feature vector based on the historical monitoring data of all stations in a monitoring region, and predicting the historical data of atmospheric pollution factor concentration through a factor decomposition machine; combining the historical data of the atmospheric pollution factor concentration with meteorological parameters to train a long-short-term memory neural network, and predicting future data of the atmospheric pollution factor concentration through the trained long-short-term memory neural network; and training the radial basis neural network by combining the future data of the atmospheric pollution factor concentration with the meteorological parameters and the geographic latitude and longitude of the station, and predicting the future data of the atmospheric pollution factor concentration of the target position point in the monitoring area through the trained radial basis neural network. The method can solve the problems that in the current atmospheric pollution factor monitoring technology, a large number of monitoring devices are needed, atmospheric pollution factor concentration prediction is inaccurate, and the future distribution trend cannot be dynamically analyzed.

Description

technical field [0001] The invention relates to the field of prediction of air pollution factors, in particular to a method and system for predicting the temporal and spatial distribution of the concentration of air pollution factors. Background technique [0002] The existing means of predicting air pollution factors are as follows: through linear regression model (linear regression model is a mathematical model in machine learning, which assumes that there is a linear relationship between input and output, and approximates this by linear superposition. linear relationship) to realize site prediction, through bicubic spline interpolation algorithm (interpolation algorithm is a data fitting or function approximation technology, through data to establish a mathematical model that approximates the real functional relationship, usually in 1-dimensional or 2-dimensional space Interpolation curve or surface) forecasting, and time forecasting through differential autoregressive sl...

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Problems solved by technology

[0033] The technical problem to be solved by the present invention is to provide a method and system for predicting the temporal and spatial distribution of air pollution factor concentration, which can solve the problem of requiring a large number of monitoring equipment in the current air pollution factor monitoring technology. , Inaccurate prediction of the concentration of air pollution factors and inability to dynamically analyze future distribution trends

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