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Construction method of spatial variable coefficient PM2.5 concentration estimation model based on Re-ESF algorithm

A technology for building methods and estimating models, applied in the application field of spatial statistical analysis services, it can solve the problems of insufficient consideration of spatial random variation and cumbersome model solving process, and achieve the effect of shortening model solving time and optimizing fitting results.

Active Publication Date: 2019-05-21
WUHAN UNIV
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Problems solved by technology

However, the calculation process of the model is cumbersome, and the calculation of the correlation coefficient requires iterative operations. In addition, insufficient consideration is given to the spatial random variation

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  • Construction method of spatial variable coefficient PM2.5 concentration estimation model based on Re-ESF algorithm
  • Construction method of spatial variable coefficient PM2.5 concentration estimation model based on Re-ESF algorithm
  • Construction method of spatial variable coefficient PM2.5 concentration estimation model based on Re-ESF algorithm

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[0058] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0059] The problem to be solved by the present invention is: the insufficient number of ground monitoring points makes it difficult to obtain the continuous PM2.5 concentration distribution in a wide range, and the ground PM2.5 concentration is affected by spatial factors, and the traditional linear regression method cannot be accurately carried out. Concentration estimates. In view of these problems, the present invention constructs a ground PM2.5 concentration model based on the remote sensing image data by using the eigenvector space filtering method, and then makes a PM2.5 distribution map.

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Abstract

The invention discloses a construction method of a spatial variable coefficient PM2.5 concentration estimation model based on a Re-ESF algorithm. The construction method comprises the following steps:step 1, multi-source AOD product fusion; step 2, data processing of related influence factors and PM2.5 concentration; step 3, obtaining independent variable values and PM2.5 concentration values ofall parts of the monitoring site; step 4, constructing an adjacent matrix; step 5, centralizing the spatial adjacency matrix and calculating a feature value and a feature vector; step 6, solving a regression coefficient of an SVC model based on the Re-ESF; step 7, judging whether variables in the model are remarkable or not; Step 8, model precision evaluation; step 9, 10-fold cross validation; step 10, judging the relation between the number k of neighborhoods and the number n of monitoring station points; and step 11, selecting an optimal model according to the precision evaluation. On the basis of effectively eliminating the influence of spatial heterogeneity and spatial autocorrelation on PM2.5 concentration modeling, a random effect and spatial variable coefficient method is introduced, so that the precision of the concentration estimation model is further improved, and the model resolving time is shortened.

Description

technical field [0001] The invention relates to the technical field of spatial statistical analysis service application, in particular to a method for constructing a spatial variable coefficient PM2.5 concentration estimation model based on a Re-ESF (random effect feature vector spatial filter) algorithm. Background technique [0002] As the primary air pollutant affecting most cities in my country, PM2.5 has attracted a lot of attention. PM2.5 refers to the aerodynamic diameter of atmospheric particles ≤ 2.5μg / m 3 of fine particles. A large number of studies have shown that high concentrations of PM2.5 have adverse effects on human health, such as the occurrence of cardiopulmonary diseases, respiratory system, cardiovascular system, nervous system, and immune system. [0003] With the establishment of monitoring stations across the country, it is possible to study regional PM2.5 concentrations. According to the data of the monitoring station, many scholars have done a lo...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06F17/18
Inventor 陈玉敏谭黄元张静祎方涛曹吉平黄黎恒罗凤兰廖佳鑫
Owner WUHAN UNIV
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