Air pollutant concentration forecasting method and device and storage medium

Abstract

The invention discloses an air pollutant concentration forecasting method and device and a storage medium. The method comprises the following steps: constructing a training set, a verification set and a test set according to a data set, wherein the data set is obtained by collecting pollutant concentration data and meteorological data of a target area within a preset time length; constructing an adjacent matrix A of a graph structure according to spatial distribution of monitoring stations in a target area; establishing a neural network model F (x;theta|A), wherein x is input data of a neural network model and comprises pollutant concentration data and meteorological data in a set time period; training the neural network model by using data of a training set, adjusting a parameter theta of the neural network model by using data of a verification set and data of a test set, and obtaining a corrected neural network model; and forecasting the air pollutant concentration by using the corrected neural network model.

Description

technical field [0001] This article relates to but is not limited to air pollutant concentration forecasting technology, especially relates to a method, device and storage medium for air pollutant concentration forecasting. Background technique [0002] The method of monitoring through air quality monitoring stations is the most commonly used method for air quality perception and air pollution observation, which has the characteristics of high measurement accuracy and good stability. However, the spatial distribution of monitoring stations in my country is too sparse, and it is difficult to provide effective and accurate data for analysis and research. Therefore, in view of the current status of data acquisition, it is very important to adopt reasonable data analysis methods in order to effectively analyze air pollution particles. [0003] In some technologies, data modeling methods for air pollutant concentrations mainly include theoretical-based methods and statistical-ba...

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Problems solved by technology

[0003] In some technologies, data modeling methods for air pollutant concentrations mainly include theoretical-based methods and statistical-based methods. These methods can predict the current or future air pollutant concentration based on historical air pollutant concentration monitoring data. , but none of them can integrate and utilize the temporal and spatial dynamic characteristics of air pollutants, and the generalization ability and forecast accuracy are average

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