Small-scale air quality index prediction method and system for city

Abstract

The invention discloses a small-scale air quality index prediction method and system for a city, which firstly divides a city area into a plurality of to-be-predicted locations in a grid form; and then acquires historical data related to each model, and based on historical data: establishing corresponding correspondences The current time prediction and the time prediction model predicted at each moment in the future time, establish a spatial prediction model for air quality prediction at the specified coordinates, and establish a dynamic prediction model that characterizes the relationship between traffic data and geographic interest point data and air quality index, an indoor and outdoor prediction model that characterizes the relationship between the indoor air quality index and the outdoor air quality index; when performing the prediction, the established time prediction model, the spatial prediction model, and the dynamic prediction are performed for any real-time moments to be predicted. The model and the indoor and outdoor prediction models are cooperatively trained to fuse the prediction results of all the models, that is, the predicted values of the air quality index at each moment in the respective current and future time periods of each to-be-predicted location.

Description

technical field [0001] The invention relates to the technical field of air quality index prediction, in particular to a method and system for predicting urban small-scale air quality index based on machine learning algorithms. Background technique [0002] With the advancement of urbanization and industrialization, the problem of environmental pollution is becoming more and more serious. In recent years, extensive and serious air pollution has directly threatened people's health and affected the green and sustainable development of social economy. At present, most regions only provide city-level air quality index forecasts, but cannot be accurate to each geographical location within the city. For residents living in cities, accurate and reasonable air quality forecasts will help them arrange production and life, adjust travel patterns and take corresponding protective measures, thereby reducing the damage of air pollutants to the body and improving the overall health of the...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved in the present invention is: to use the collaborative training algorithm that combines multiple prediction methods to predict the air quality index for each geographic location point within the urban range near the air quality monitoring base station, while keeping the calculation at a low level. While increasing the complexity, improve the accuracy of prediction

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