Causal centrality-based haze analysis and identification method

Abstract

The invention discloses a causal centrality-based haze analysis and identification method. The method comprises the steps of obtaining monitoring data detected by a plurality of detection sites; constructing an intra-region data association model and an inter-region data association model for different monitoring factors; calculating a causal relationship value between each monitoring factor and the haze concentration; forming a causal matrix according to the causal relationship values; according to the causal matrix, calculating a causal centrality characteristic value for representing the topological centrality of each detection site; and inputting the causal relationship value and the causal centrality characteristic value between the monitoring factors into the established prediction model, and training the prediction model to obtain a trained prediction model. According to the haze analysis and identification method based on the causal centrality, the causal relationship and complex network centrality analysis method is combined, the meteorological data and the industrial waste gas emission data are modeled, the characteristics of the haze causal association degree, the directivity information and the like between regions are fully utilized, and the haze weather identification and prediction level is remarkably improved.

Description

technical field [0001] The invention relates to a haze analysis and recognition method based on causal centrality. Background technique [0002] Haze weather seriously affects people's life and health. Especially in North China, the Yangtze River Delta and central China, these areas are densely populated and economically developed, and the demand for natural resources is much higher than other areas in China. With the increase of fossil fuel consumption in factories and private cars, sulfur dioxide and nitrogen oxides emitted into the air not only cause direct harm to humans and plants, but also cause secondary pollution such as acid rain, smog, greenhouse effect and photochemical smog. Severe smog pollution has also emerged in many developed countries. As the main culprit of air pollutants, PM2.5 concentration increases mortality from respiratory and cardiovascular diseases. [0003] Methods for air quality assessment can be divided into three categories, based on physic...

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Problems solved by technology

However, feature selection in machine learning is a hard problem

After the iterative solution, there is no general criterion for determining whether the features are suitable until the prediction results are available

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