3.1 Time distribution of agglomerate fog
This article counts all the high-speed road sections with frequent agglomerate fog in Shandong Province in one year, and analyzes the number of agglomerate fog occurrences by month and time period. As shown in Figure 3,agglomerate fog occurred the most in November, with a total of 1966 times, followed by 1742 times in December and 1613 times in March, and the number of occurrences decreased sharply from March to August. In summer, the number of occurrences in June, July, and August was less than 100. The correlation coefficient between the month and the number of agglomerate fog was R2 of 0.54, which was relevant. ; The agglomerate fog occurred the most times from 5:00 to 6:00, totaling 2006 times, and the overall downward trend from 6:00 to 17:00. The number of occurrences from 20:00 to 9:00 on the next day was more than 700, the correlation coefficient R2 is 0.63, which has a strong correlation. To sum up, it can be seen that the time of agglomerate fog has a strong regularity, and it mostly occurs in autumn and winter with large temperature differences, as well as night and morning peak periods.
3.2 Spatial distribution of road sections with frequent agglomerate fog
From the linear density analysis, it can be seen that the Tuanwu-prone sections gradually decrease from east to west, and the eastern part is dominated by mountains and hills and the coastal areas are densely distributed, as shown in Figure 4. Among them, the Shenhai Expressway, Rongwu Expressway and other coastal highways where fog frequently occurs are mostly, accounting for about 1/4 of the total road sections. Therefore, considering that the frequent occurrence of group fog is related to hydrology and topography, the analysis is conducted in two aspects.
3.3 Correlation analysis of the causes of agglomerate fog based on GWR
To ensure the validity of the GWR model, it is necessary to test the spatial correlation of the variables in the study area. If the spatial correlation of the variables is not significant, it means that the distance between discrete points has little effect on the value, and the geographical attributes of the variables have little effect on the value. The influence of the variable value is small, and the use of geographically weighted regression is not a necessary choice. The Moran I index in the spatial autocorrelation tool in ARCGIS is used for analysis. A positive value of the Moran index indicates a positive spatial correlation, and a negative value indicates a negative spatial correlation. The analysis results of the distribution of the number of cluster fog occurrences are shown in Figure 5. It shows that the Moran index is 0.86, indicating that there is a strong spatial correlation and aggregation, that is, the number of occurrences of cluster fog in a certain area is related to the location of the area. The Z score is about 25, indicating that it is 25 times the standard deviation. The results are distributed at both ends of the normal distribution.
This study uses the GWR model to quantitatively analyze the relationship between the number of agglomerate fog occurrences and the river density, elevation and NDVI. Based on the GWR model, the relationship between the number of agglomerate fog occurrences and the river density, the elevation difference of the area where the agglomerate fog occurs, and NDVI are analyzed respectively. The comparison of the analysis results output by GWR is shown in Table 3.
Tab.3 Comparison Table of Analysis Results when the Dependent Variable is the Number of Agglomerate Fog Occurrences
Explanatory variables
|
GWR R2
|
GWR R2 Adjusted
|
Linear Regression R2
|
River density
|
0.4532
|
0.3901
|
0.1933
|
Regional elevation difference
|
0.6389
|
0.5655
|
0.2652
|
NDVI
|
0.5405
|
0.4862
|
0.1228
|
The above three
|
0.5277
|
0.4498
|
/
|
It can be seen from Table 3 that the number of agglomerate fog occurrences based on the GWR model analysis has a large correlation with river density, regional elevation difference and NDVI. The order of correlation is regional elevation difference> NDVI> river density, and relative to Ordinary linear regression model. The correlation coefficient R2 between the number of agglomerate fog occurrences based on the GWR model and the river density, regional elevation difference and NDVI model is larger. Therefore, the accuracy of the GWR model is much higher than that of the ordinary linear regression model, and the number of agglomerate fog occurrences It has strong spatial heterogeneity, refines the distribution characteristics of the frequently-occurring road sections, and characterizes the local spatial changes of the geographical environment of each frequently-occurring road section.
Each variable has a promoting or inhibiting effect on the number of agglomerate fog occurrences in different areas, which is represented by the positive and negative conditions of the regression parameters. The results of the GWR model analysis show that: (1)In the results of river density analysis, 63% of the grid regression parameters are negative, which means a significant negative correlation. The higher the river density, the fewer the occurrence of agglomerate fog; (2) In the analysis results of the regional elevation difference, 62% of the grid regression parameters are positive, that is, a significant positive correlation. When the elevation difference in the region is greater, the number of agglomerate fog occurrences is greater;(3) In the NDVI analysis results, 64% of the grid regression parameters are negative, which means a significant negative correlation. When the NDVI index in the area is larger, the agglomerate fog is negative, the fewer occurrences of agglomerate fog. The main reason for the formation of agglomerate fog is the generation of temperature inversion layer, and the river has the function of regulating the temperature difference between day and night, so it has a certain inhibitory effect on the occurrence of cluster fog; NDVI can reflect the coverage of plant canopy, and it can greatly change the sun when there are more vegetation. Radiation, regulating the surface temperature and humidity, has a certain inhibitory effect on the occurrence of cluster fog; when the elevation difference is large, it may cause a large temperature difference in the area, and have a certain impact on precipitation and wind speed, thus having a certain promotion effect on the occurrence of agglomerate fog. Because the river density, NDVI, and elevation difference have a negative effect on the number of cluster fog, when the three are used as explanatory variables, the correlation decreases.
3.4 Analysis of GWR model prediction results
(1) When the condition number in the regression analysis result is less than 0 and greater than 30 or is set to "empty", it means that there is strong local multicollinearity, and the reliability of the associated results is low. According to the regression results of the three models, the regression condition number of river density is between 1-3, the regression condition number of elevation difference is between 2-8, and the regression condition number of NDVI is between 1-30, that is, there is no difference between the independent variables. There is a problem of collinearity, which meets the requirements of regression analysis, and the test results of the GWR model are credible;
(2) Feedback from the GWR model result analysis table, the predicted value with a difference of less than 3 from the actual number of agglomerate fog occurrences accounted for 89.86%, and the predicted value with a difference of less than 5 accounted for 96.66%, and the prediction results were more accurate. .
3.5 Autocorrelation analysis of input parameters
(1) From the analysis based on the GWR model, it can be seen that river density, elevation difference and NDVI have a strong influence on the number of agglomerate fog occurrences in the area. The correlation between the three parameters is analyzed, and the correlation coefficients between the two are less than 0.1, no obvious correlation;
(2) Perform variance inflation factor (VIF) test and collinearity test on the independent variables of regional river density, elevation difference and NDVI. Among them, the river density and elevation difference VIF=1.01<7.5, NDVI VIF= 1.0<7.5, which means that the requirements of regression analysis are met, there is no obvious collinearity problem, and the results of the GWR model are credible.
3.6 Error analysis based on GWR model
The study area is divided into 778 grids. The standard deviation of each grid is calculated through the GWR model with river density, elevation difference and NDVI as independent variables. Among them, there are 9 grids with standard deviations >2.5 or <-2.5. Rong-Wu Expressway and Shen-Hai Expressway are mainly concentrated in the eastern coastal areas, as shown in Figure 6. Since the eastern coastal areas are mostly undulating and undulating hilly areas, complex terrain conditions have an impact on meteorological conditions such as precipitation, air humidity, wind direction and wind speed, leading to large errors in forecasting agglomerate fog.
The darker the color of the prediction area, the greater the standard deviation.Map created in ArcMap 10.2 of the Environmental System Resource Institute, Inc. (www.esri.com/software/arcgis/arcgis-for- desktop).
3.7 Correlation analysis of cluster fog incentives based on MGWR model
Take the center point coordinates of 778 grids and the corresponding river density, elevation difference, and NDVI, and import them into MGWR2.2.
Tab.4 Fitting Results based on the MGWR Model
Explanatory variables
|
Regression coefficients
|
Corrected regression coefficient
|
Model bandwidth based on 3 independent variables
|
River density
|
0.593
|
0.566
|
599
|
Regional elevation difference
|
0.771
|
0.659
|
314
|
NDVI
|
0.637
|
0.595
|
44
|
The above three
|
0.649
|
0.605
|
-
|
The results are shown in Table 4. The regression effect is better than the GWR model, that is, the multi-bandwidth of a specific bandwidth is calculated according to the independent variables. The model is better than the single bandwidth model. Since this model is currently only suitable for fitting regression and cannot achieve the prediction effect of the GWR model, this method can be used as a preliminary fitting verification, and it can also be combined with GIS to develop its prediction function to further improve the prediction effect.