(13) Step 3 (sample class attribute recognition). — Class attribute identification is in accordance with selleck the confidence value λ: If ki=mink:∑l=1kuil≥λ,k=5,4,3,2,1Then Xi Can be considered as class Ck, (14) where λ normal circumstances take 0.6 ≤ λ ≤ 0.7. Step 4 (security score calculations). — Assuming
each evaluation category Ck corresponding score of qk, then the combined attribute security score is Si=∑k=14uikqk. (15) 4. Case Studies 4.1. Chinese Regions Environment Overview Five domestic environmental factors such as rainfall, lightning, wind, temperature, and earthquake in recent years are collected from 2002 to 2012 as the basic assessments data [17] as is shown in Table 6. (The data of rain factor is summary of annual average rainfall in various regions, the data of thunder and lightning factors comes from various regions’ monitoring reports, and the data of wind factor represents the influence extent by
monsoon in various regions.) Table 6 Chinese regional environment situation in recent years from 2002 to 2012. The program of MATLAB is employed to work out the estimation. The specific method is made by 31 districts samples and each has 9 indexes. Then we constitute the sample matrix R31×9. There are five characteristics consisting of particularly serious, severe, moderate, mild, and no effect, whose intermediate values will be made up of attribute matrix R5×9; that is, R5×9=35.032.527.522.510.03.002.501.500.750.2530.026.019.513.04.505.205.004.604.151.951.000.800.450.200.0555.050.040.022.57.5029702475144075030064.056.041.530.017.5−20.0−15.0−5.002.507.50.
(16) Use the function pdist of MATLAB to work out the Mahalanobis distance between the districts sample and the attribute class: z=pdist(R31×9,R5×9,“mahal”), (17) where z is the Mahalanobis distance matrix between the sample and the attribute and mahal is representing the use of the function Mahalanobis distance to work out the distance of matrix. Then make confidence level λ = 0.60, and each of the area’s environmental attribute recognition values and attribute classification can be obtained as that in Table 7. Table 7 Chinese regional environment impacts attribute Entinostat recognition value of high speed railway. The calculation results in the above table show that the environmental safety situation of Xinjiang, Sichuan, Heilongjiang, and Jilin belongs to serious category, which takes up 12.9%. The situation in the Medium level areas accounts for 32.2%, such as Heilongjiang, Hebei, Liaoning, Jiangsu, and Guangdong, and that of the 17 areas such as Beijing, Tianjin, Guizhou, Gansu, and other regions belongs to slight level, which accounts for 54.9%. It is notable that, in addition to Sichuan, the high speed railway environment impacts in the serious level areas are mostly distributed in coastal areas and northern regions, while Chinese abdominal regions are mostly in the medium and light level (see Figure 2).