research article high speed railway environment safety...

Research ArticleHigh Speed Railway Environment Safety Evaluation Based onMeasurement Attribute Recognition Model

Qizhou Hu1 Ningbo Gao1 and Bing Zhang2

1 School of Automation Nanjing University of Science amp Technology Nanjing Jiangsu 2100984 China2 East China Jiaotong University Nanchang Jiangxi 330013 China

Correspondence should be addressed to Ningbo Gao 916747600qqcom

Received 20 July 2014 Revised 22 September 2014 Accepted 25 September 2014 Published 9 November 2014

Academic Editor Yongjun Shen

Copyright copy 2014 Qizhou Hu et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

In order to rationally evaluate the high speed railway operation safety level the environmental safety evaluation index systemof highspeed railway should bewell established bymeans of analyzing the impactmechanismof severeweather such as raining thunderinglightning earthquake winding and snowing In addition to that the attribute recognition will be identified to determine thesimilarity between samples and their corresponding attribute classes on the multidimensional space which is on the basis ofthe Mahalanobis distance measurement function in terms of Mahalanobis distance with the characteristics of noncorrelation andnondimensionless influence On top of the assumption the high speed railway of China environment safety situation will be wellelaborated by the suggested methods The results from the detailed analysis show that the evaluation is basically matched up withthe actual situation and could lay a scientific foundation for the high speed railway operation safety

1 Introduction

According to the high speed railway safety operation researchcarried out in the laboratory of Nanjing University of Scienceand Technology the high speed railway operation failuredirectly caused by bad environments accounts for 29 fromJuly 2011 to December 2012 and comparatively the speedrailway accidents in severe weather take up 814 of the totalones at the same timeThe above statistics thus give us a betterunderstanding of the fact that the bad weather has significanteffects on the high speed railway safety operation

In China the current researches of environment impacton high speed railway can be mainly divided into the follow-ing two categories first themacrodisaster emergency predic-tion and warning system design and second the microen-vironmental factors impact mechanism analysis As to thefirst one Sun et al Wang et al and Tao et al have outlinedsome key problems of high speed railway environment safetysuch as alarm threshold the layout ofmonitoring points traincontrolling mode and the basic component of high speedrailway warning system [1ndash3] Xiao et al Calle-Sanchez et aland Wang et al also made an analysis of the potential factors

which caused railway disaster from the following four aspectspersonnel equipment management and environment [4ndash6] And Miyoshi and Givoni introduced analytic hierarchyprocess to set up railway environmental risk assessmentsystem [7] In the aspect of environmental factors impactmechanism Zhou and Shen Ling et al and Lee et al havemade a specific discussion of such impact mechanism suchas earthquake wind and other disasters in high speed railwayfrom the view of engineering construction [8ndash10]

The comparison of the studies from abroad and homereveals that the researches of the high speed railway environ-ment safety have been repeatedly carried out in an extremelyearlier time and have been carefully studied by a lot of foreignresearchers Many countries have built up their own efficienthigh speed railway disaster warning system such as theHokkaido and Shinkansen disaster warning system in Japanwhich leads many other countries to conduct the earthquakeprediction For instance France is now in possession of itsMediterranean earthquake monitoring system and Germanyowns high speed railway disaster prevention system Thoughthe disaster monitoring systems of JingJingtang Fuxia andWuguang have been already built in China Zhang and Zeng

Hindawi Publishing CorporationComputational Intelligence and NeuroscienceVolume 2014 Article ID 470758 10 pageshttpdxdoiorg1011552014470758

2 Computational Intelligence and Neuroscience

Table 1 High speed railway mechanism analysis of environmental impact factors

Environmentalfactor Mechanism

Rainfall

(i) Raining is the foremost factor that is easily causing line fault Additionally the current flow will emerge betweenthe pantograph and overhead line systems of the high speed railway when it comes to a heavy rainy day and the trainpower supply will also be consequently influenced(ii) Rainfall can cause mountain soil landslides that will directly lead to the abnormal operation of the train

Cross wind

(i) The mechanism of the influence caused by horizontal wind on the high speed railway is that it can produce theyawing force which will allow the lateral migration Moreover the produced lift force will lead to the trainderailment through the pneumatic action with the high speed train which will undeniably increase the risk of trainbeing derailed

Lightning (i) Lightning can disrupt the power supply of the high speed railway train traction which will result in the suddenstop of the high speed rail train through breaking down the high speed railway along the circuit devices

Earthquake(i) Earthquake wave can be divided into two kinds the P wave (primary wave pressure wave) and the S wave(secondary wave shear wave) S wave can destroy the building structure and cause the landslides orbital shift andtrain wheel derailment which will influence the safe driving of high speed railway

Temperature

(i) High temperature can lead to a big temperature difference between the internal and external the increase of airconditioning power and the aggravation of the train power supply load Besides high temperature can cause theshort circuit because of the softened line(ii) Cold damp climate will lead to the ice covering membrane on the railway track which will reduce the frictionbetween train and track and increase the risk of a derailment when the train is at high speed turning

Snowfall (i) A lot of snow will cover the track and the ice on the track will increase the degree of danger of the train operation

contend that all the systems can be still well improved on thebasis of the original ordinary railway disaster warning system[11] because there is a certain gap between foreign andChinarsquoshigh speed railway disaster warning systems after a relativelyfair comparison

Through the comparison of present researches betweendomestic and foreign we can find that the domestic highspeed railway disaster prevention is now in a transition fromtheory to practice while foreign high speed railway disasterprevention system has been at a relatively perfect stageTherefore it is an urgentmission for the domestic researchersto make an intensive effort to the theory research of highspeed railway disaster protection and system constructionprocess so as to promote China high speed railway operatingsafety level

2 High Speed Railway Environmental ImpactEvaluation Indexes

21 High Speed Railway Index System of EnvironmentalImpacts The operational problems of the high speed railwayare mainly caused by such uncertain factors as raining thun-dering and lightning horizontal wind earthquake and soforth whose degree of intensitywill directly decide the degreeof danger posing to the high speed railway operation safetyThe analysis of the characteristics of various environmentalfactors in the process of high speed railway operation inrecent years and the conclusion of themechanism of differentenvironmental factors on high speed railway safe operationare presented in Table 1

Besides the six factors listed in Table 1 problems in thehigh speed railway are also being influenced by debris flowand water and rock burst However given the complexity ofgeological conditions and the difficulty of data acquisition we

only use average annual rainfall average annual maximumlightning density annual disaster monsoon winds averagedisasters incidence of monsoon average magnitude gradeaverage incidence of earthquakes average annual maximumsnow depth average highest temperature and average min-imum temperature as the environment factor evaluationindex which are shown in Figure 1

It is necessary to bementioned that the usual climate envi-ronment will not exert any influence upon the operation ofhigh speed railway except typhoon sandstorm blizzard andearthquakes while high or low temperatures have significantinfluence on the operation of high speed railway Thereforewith the exclusive of the average rainfall in Figure 1 otherfactors represent the extreme climate environment Eachenvironmental factor evaluation index calculation formulaand specification is shown in the following equations

Average annual rainfall level is

AAR =119873

sum

119894=1

RF119894

119873

(1)

where RF119894is the maximum rainfall in the 119894th year (mm) and

is the number of the yearsMaximum lightning density is

MLD =

119873

sum

119895=1

LH119895

(119873 times area) (2)

where LH119895is the thunder lightning happening in certain

region in the 119895th year (time) and area is the area of a city orregion (m2)

Disasters wind speed is

AWS =119882119899

sum

119896=1

DWS119896

119882119899

(3)

Computational Intelligence and Neuroscience 3

Uncertainty factors of high speed railway

Wind factor Earthquake factor Rain factor Temperature factorSnow factor Lightning

Aver

age w

ind

spee

d

Aver

age s

now

dep

th

Aver

age m

agni

tude

gra

de

Aver

age l

ight

ning

den

sity

Aver

age a

nnua

l rai

nfal

l

Aver

age h

igh

tem

pera

ture

Aver

age l

ow te

mpe

ratu

re

Aver

age w

ind

happ

enin

g

Aver

age m

agni

tude

hap

peni

ngFigure 1 High speed railway environmental impact evaluation indexes system

Table 2 Japanese Shinkansen winds threshold

Wind scale Wind speed (ms) The impact with no wind-break wall The impact with wind-break wall80-90 20ndash25 The train speed under 160 kmh No speed limit90ndash104 25ndash30 The train speed under 70 kmh The train speed under 160 kmh104ndash125 30ndash35 Off-stream The train speed under 70 kmhAbove 125 Above 35 Off-stream Off-stream

where DWS119896is the speed of the 119896th disaster wind (ms) and

area is the area of a city or region (Km2)Average wind happening is

AWH =

119882119899

119873

(4)

where 119882119899is the total times of the disaster wind happening

(time)Average magnitude grade is

AMG =

119863119899

sum

119897=1

MG119897

119863119899

(5)

where MG119897is the magnitude of the 119897th earthquake (degree)

and119863119899is the total times of the earthquake (time)

Average magnitude happening is

AWH =

119863119899

119873

(6)

Average high and low temperature are

AHT =119873

sum

119901=1

Max119879119901

119873

ALT =119873

sum

119901=1

Min119879119901

119873

(7)

where Max119879119901is the highest temperature in the 119901th year (∘C)

and Min119879119901is the lowest temperature in the 119901th year (∘C)

Average snow depth is

ASD =

119873

sum

119903=1

Max SD119903

119873

(8)

where Max SD119903is the deepest depth in the 119903th year (cm)

22 Demarcation of the Environmental Climate FactorAffected Threshold (Modify)

221 Threshold under Horizontal Wind Influence The rep-resentative research about the effects of horizontal wind onhigh speed railway train running is conducted preciously inJapan which calculates the horizontal wind velocity underthe condition of critical capsize under different runningspeed by wind tunnel experiment and takes the criticalwind speed as the threshold of Shinkansen disaster warning(Table 2) Chinarsquos high speed railway line train CRH series arecharacterized by the similar features with those of Japanesetrain in the shape and the axle load Therefore the JapaneseShinkansen warning horizontal wind speed is adopted asthe influencing factors of high speed railway in our countryhorizontal wind threshold

222 Threshold under Earthquake Influence In terms ofthe research results at home and abroad the calculation ofearthquake alarm threshold (EAT) of high speed railway canbe referred to as the following formula (9)

EAT = 119860119863

(9)


Table 3 Earthquake magnitude threshold of high speed railway (119863 takes 255)

Rank Very serious Serious General Slight No effectTrain lateral acceleration (119860) 240Gal 180Gal 120Gal 60Gal 0GalEarthquake magnitude (EAT) gt52 48 44 39 lt39

Table 4 The annual rainfall threshold of high speed railway

Rank Very serious Serious General Slight No effectAnnual rainfall gt2970mm 1980mm 900mm 600mm lt600mm

where 119860 is the maximum lateral acceleration thresholdensuring that the normal operation of the train can withstandwithout orbit (Gal) 119863 is the maximum dynamic responsecoefficient of various structures of railway under differentseismic wave excitation and suggestive value is 255

Researches show that when

case 119860 ge 120Gal the train begins to pourcase 119860 ge 240Gal the train will completely overturn

Therefore we define 119860 = 240Gal and 119860 = 120Galas the threshold of strong impact and general impact onthe safe operation of the high speed railway train Andthe earthquake magnitude threshold of high speed railwayoperation is calculated by different value method which isshown in Table 3

223 Threshold under Rain Influence Domestic railwaydepartment limits the train running speed based on the sizeof the rain

If the rain runs moderately which lasts 12 (or 24)hours and the rainfall capacity arrives at 100mmndash229mm(17mmndash379mm) its speed should be reduced

If the rain runs in a heavy rainy day which lasts 12 (or 24)hours and the rainfall capacity reaches 230mmndash499mm(330mmndash749mm) the railway lines are supposed to beblocked and the train operation is supposed to be prohibited

For the sake of dimensional consistency we can turnthe hour rainfall volume into annual rainfall volume bythe following method it is universal knowledge that ourcountryrsquos rain season will experience a period of 3 monthsthat can be calculated by 12 rainfall times thus we categorizethe annual rainfall volume into 900mm 1980mm and2970mm respectively as the moderate rainfall city heavyrainfall city and the storm rainfall city Accordingly we cancalculate rainfall threshold effects on high speed railwaycompared with the provisions of the railway departments inTable 4

224 Other Environmental Factors Threshold The currenttheoretical researches both at home and abroad pay less atten-tion to the lightning snowing temperature and snowfallwhich will definitely bring some influences on the charac-teristics of the high speed railway operations Because it isdifficult to set up a uniform standard to measure the factorsexperts suggest that the reference value and the method ofcombining qualitative analysis can be employed to determine

what degree of lightning snow and temperature influencingthe high speed rail threshold The environment impactassessment index of high speed railway can be discriminatedas in Table 5

3 High Speed Railway Environmental ImpactsAttribute Recognition Model

Attribute recognition model is in essence the problems ofmultidimensional space between sample and attributionwhich is proposed by professor Cheng and has been widelyused in evaluation and classification The sample space 119883 =

1199091 1199092 1199093 119909

31 has been calculated in 31 provinces and

autonomous regions in our country among which eachhas been given nine high speed rail environmental impactindexes as 119868

119895(119895 = 1 2 9) and the 119895th environmental

impact assessment index value in the 119894th region is expressedas 119909119894119895(119894 = 1 2 31 119895 = 1 2 9) 119865 is defined on a

sample space 119883 ordered split sets where the environmentalimpact is divided into five progressive ways as serious severemoderate mild model and no effect An ordered set of splitis defined as 119865 = 119862

1 1198622 1198623 1198624 1198625 which is in accordance

with the relationship as 1198621≻ 1198622≻ 1198623≻ 1198624≻ 1198625

Each ordered set is then to be split into a collection ofenvironmental evaluation threshold segmentation classes Tomake a clear illustration of the ordered stripe set a standardform has been set up as follows

1198681

1198682

1198689

11986211198622119862311986241198625

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

1198861111988612119886131198861411988615

1198862111988622119886231198862411988625

1198869111988692119886931198869411988695

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

(10)

where 119886119894119895(119894 = 1 2 9 119895 = 1 2 3 4 5) 119886

1198941gt 1198861198942gt 1198861198943gt

1198861198944gt 1198861198945

The value of the sample properties has attributes char-acterized by a sample 119883

119894and expressed as 119906

119894119896= 119906 (119906

119894isin

119862119896) among which the measurement function is the core of

attribute recognition model Hu et al Yan and Xiao et almake an analysis of the usual linear discriminated functionwhose accuracy is less than that of a nonlinear functionTherefore the recent researches have found that the normaldistribution function is used much more frequently whileother nonlinear functions are often being regarded as anattribute identification measure function [12ndash14] Howeverthe normal distribution function as a measure function has


Table 5 High speed railway environment impact assessment index discrimination safety threshold

Environment Evaluation Index Particularly serious Serious Medium Slight No effect1198621

1198622

1198623

1198624

1198625

Cross wind 1198831(ms) gt30 25ndash30 15ndash25 5ndash15 0ndash5

1198832(time) gt3 2-3 1-2 05ndash10 0ndash05

Snowfall 1198833(cm) gt30 22ndash30 17ndash22 9ndash17 0ndash9

Earthquake 1198834(level) gt52 48ndash52 44ndash48 39ndash44 0ndash39

1198835(time) gt10 06ndash10 03ndash06 01ndash03 0-01

Lightning 1198836(timekm2) gt55 45ndash55 30ndash45 15ndash30 0ndash15

Rainfall 1198837(mm) gt2970 1980ndash2970 900ndash1980 600ndash900 0ndash600

Temperature 1198838(∘C) gt64 48ndash64 35ndash48 25ndash35 10ndash25

1198839(∘C) ltminus20 minus10ndashminus20 minus10ndash0 0ndash5 5ndash10

1198831 average disaster annual wind speed1198832 the annual incidence of disasters monsoon1198833 annual maximum snow depth1198834 the average magnitude level1198835 average annual rate of earthquake occurrence1198836 annual lightning density1198837 annual maximum rainfall1198838 average annual maximum temperature1198839 average annual minimum temperature

its shortcomings because data should be standardized beforehandling bias and the separated index weights should alsobe determined What is more the last attribute recognitionresult is relative

However there is no certain way to evaluate the relativeimportance of objective indicators in a fairly wayThe essenceof attribute recognition is to determine the attributes spacesimilarity and methods used to calculate the spatial distanceare Euclidean distance Ming distance and Mahalanobisdistance Todeschini et al and Kayaalp and Arslan assert thatthe Mahalanobis distance has the advantages of weakeningthe correlation between impact indicators and automaticweight in the index calculation based on data changes [15 16]

Therefore in order to compensate for normal functionwe use Mahalanobis distance as the measurement functionto build the attribute recognition model

Step 1 (Mahalanobis distance between sample and attributeclass calculations) Assuming the sample119883

119894has been an area

of environment evaluation the sample Mahalanobis distancewith the attribute class 119862

119896is

119889119894119896=radic(119883119894minus 119862119896) Σ

minus1

119894119896(119883119894minus 119862119896)

119879

(11)

where 119883119894= (1199091198941 1199091198942 119909

1198949) representing the 119894th region

environment factor evaluation vector and 119862119896

= (1198861198961

1198861198962 119886

1198969) representing each classification criteria value of

environmental factors on the properties class 119896 vector Σ119894119896=

the covariance matrix between119883119894and 119862

119896is

Σ119894119896=

[

[

[

[

Cov (1199091198941 1198861198961) Cov (119909

1198941 1198861198962) sdot sdot sdot Cov (119909

1198941 1198861198969)

Cov (1199091198942 1198861198961) Cov (119909


1198942 1198861198969)

sdot sdot sdot sdot sdot sdot sdot sdot sdot sdot sdot sdot

Cov (1199091198949 1198861198961) Cov (119909


1198949 1198861198969)

]

]

]

]

(12)

where Cov(119909 119910) = 119864[(119909 minus 119864(119909))(119910 minus 119864(119910))]

Step 2 (standard attribute measurement value calculations)Generally the greater the similarity of Mahalanobis distancethe smaller the measurement valueTherefore assuming thatMahalanobis distance between area119883

119894and attribute class 119862

119896

has been derived 119889119894119896 the standard attribute measurement

value is

119906119894119896=

1119889119894119896

sum

5

119895=1(1119889119894119895)

(13)

Step 3 (sample class attribute recognition) Class attributeidentification is in accordance with the confidence value 120582

If 119896119894= min119896

119896

sum

119897=1

119906119894119897ge 120582 119896 = 5 4 3 2 1

Then 119883119894Can be considered as class 119862

119896

(14)

where 120582 normal circumstances take 06 le 120582 le 07

Step 4 (security score calculations) Assuming each eval-uation category 119862

119896corresponding score of 119902

119896 then the

combined attribute security score is

119878119894=

4

sum

119896=1

119906119894119896119902119896 (15)

4 Case Studies

41 Chinese Regions Environment Overview Five domesticenvironmental factors such as rainfall lightning wind tem-perature and earthquake in recent years are collected from2002 to 2012 as the basic assessments data [17] as is shown inTable 6 (The data of rain factor is summary of annual averagerainfall in various regions the data of thunder and lightningfactors comes from various regionsrsquo monitoring reports and


Table 6 Chinese regional environment situation in recent years from 2002 to 2012

Region Index1198831

1198832

1198833

1198834

1198835

1198836

1198837

1198838

1198839

Beijing 0 0 225 0 0 1361 49896 2686 minus275Tianjin 4167 011 197 0 0 86 5046 2684 minus371Hebei 4792 022 20 78 003 2997 54497 2748 minus177Shanxi 0 0 21 0 0 2741 44346 2448 minus511Inner Mongolia 0 0 17 0 0 06 37329 2356 minus1087Liaoning 4167 011 25 73 002 875 70163 243 minus1201Jilin 2639 011 27 0 0 925 59841 2338 minus146Heilongjiang 0 0 34 0 0 1544 4989 2324 minus169Shanghai 3299 089 8 0 0 17176 109241 2939 475Jiangsu 3514 111 22 0 0 4025 116472 2881 295Zhejiang 3922 278 6 0 0 76 127682 2995 484Anhui 3687 122 15 0 0 3575 105721 2874 276Fujian 3956 322 4 0 0 353 135553 2981 113Jiangxi 388 178 12 0 0 35 150014 3013 553Shandong 338 033 17 0 0 325 82057 2698 minus121Henan 4213 033 19 0 0 3067 72481 2729 14Hubei 4067 078 17 0 0 2672 121048 2983 438Hunan 3552 078 164 0 0 29 127644 2996 839Guangdong 3393 411 0 0 0 4825 180549 2978 1316Guangxi 3492 233 4 0 0 2625 118973 283 1449Hainan 3174 222 0 0 0 3875 178062 2906 1407Chongqing 0 0 37 0 0 2358 106561 2953 689Sichuan 0 0 42 744 01 57256 84316 2596 598Guizhou 4306 011 45 0 0 3175 98978 2319 352Yunnan 3519 033 0 733 01 2721 87828 21 962Tibet 0 0 52 0 0 029 45312 1731 062Shanxi 15 2 19 0 0 1546 61111 2761 036Gansu 124 222 18 66 002 036 27174 2246 minus506Qinghai 32 356 15 69 002 042 4429 1748 minus775Ningxia 472 189 179 0 0 477 17534 2431 minus738Xinjiang 46 467 46 71 005 025 30961 2419 minus129

the data of wind factor represents the influence extent bymonsoon in various regions)

The program of MATLAB is employed to work outthe estimation The specific method is made by 31 districtssamples and each has 9 indexes Then we constitute thesample matrix 119877

31times9 There are five characteristics consisting

of particularly serious severe moderate mild and no effectwhose intermediate values will bemade up of attributematrix1198775times9

that is

1198775times9

=

[

[

[

[

[

[

[

[

[

[

[

[

[

350 325 275 225 100

300 250 150 075 025

300 260 195 130 450

520 500 460 415 195

100 080 045 020 005

550 500 400 225 750

2970 2475 1440 750 300

640 560 415 300 175

minus200 minus150 minus500 250 750

]

]

]

]

]

]

]

]

]

]

]

]

]

(16)

Use the function pdist of MATLAB to work out theMahalanobis distance between the districts sample and theattribute class

119911 = pdist (11987731times9

1198775times9 ldquomahalrdquo) (17)

where 119911 is the Mahalanobis distance matrix between thesample and the attribute and mahal is representing the use ofthe function Mahalanobis distance to work out the distanceof matrix

Then make confidence level 120582 = 060 and each ofthe arearsquos environmental attribute recognition values andattribute classification can be obtained as that in Table 7

The calculation results in the above table show that theenvironmental safety situation of Xinjiang Sichuan Hei-longjiang and Jilin belongs to serious category which takesup 129 The situation in the Medium level areas accountsfor 322 such as Heilongjiang Hebei Liaoning Jiangsuand Guangdong and that of the 17 areas such as BeijingTianjin Guizhou Gansu and other regions belongs to slight


Table 7 Chinese regional environment impacts attribute recognition value of high speed railway

Region ValueParticularly serious Serious Medium Slight No effect Classification

Xinjiang 0301 0402 0117 0003 0177

SeriousSichuan 0376 0246 0196 0120 0062Jilin 0303 0363 0146 0082 0106Heilongjiang 0269 0342 0215 0140 0034Hebei 0169 0196 0237 0231 0168

Medium

Liaoning 0201 0203 0218 0198 0180Jiangsu 0177 0209 0228 0211 0174Zhejiang 0180 0202 0225 0205 0188Anhui 0166 0202 0234 0221 0177Jiangxi 0196 0222 0206 0199 0178Hubei 0179 0210 0221 0216 0175Hunan 0175 0205 0221 0222 0176Guangdong 0195 0200 0212 0198 0196Fujian 0194 0211 0195 0200 0200Beijing 0163 0193 0226 0239 0179

Slight

Tianjin 0158 0188 0232 0234 0187Shanxi 0160 0190 0227 0228 0195Inner Mongolia 0178 0200 0202 0212 0208Shanghai 0173 0203 0215 0224 0185Hainan 0168 0198 0222 0224 0189Shandong 0162 0196 0234 0222 0186Henan 0150 0184 0247 0237 0182Guangxi 0151 0181 0222 0248 0199Chongqing 0180 0201 0208 0223 0187Guizhou 0162 0187 0202 0206 0244Yunnan 0153 0177 0201 0229 0239Tibet 0178 0192 0202 0213 0215Shaanxi 0155 0187 0235 0245 0178Gansu 0165 0191 0215 0237 0192Qinghai 0177 0194 0194 0203 0231Ningxia 0155 0183 0224 0237 0200

level which accounts for 549 It is notable that in additionto Sichuan the high speed railway environment impacts inthe serious level areas are mostly distributed in coastal areasand northern regions while Chinese abdominal regions aremostly in the medium and light level (see Figure 2)

For further analysis security score in the areas of theserious level should be calculated by means of gradingcriterion All kinds of scores are clarified in Table 8

The calculation results show that Sichuan has the lowestscores of 62460 followed by 63280 in Heilongjiang and63489 in Xinxiang and Yunnan has the highest score of7223

42 High Speed Railway Line Safety Environment AnalysisThere are 25 high speed railway operational lines in ourcountry currently which constitute the total mileage of 10192kilometers Most of the high speed railways are located insoutheast of China where complex geological accidents suchas landslip earthquake and other geological disasters take

place frequently The high speed railway environment safetysituation is clearly illustrated in Table 9

The Jinghu line Fuxia line and Huning line mainly goacross regions of Beijing Tianjin Jinan Nanjing ShanghaiHangzhou and so on Most of these regions are located inthe medium impacted or light impacted areas where rainingand storm happen frequently Thus we have to pay attentionto the influence of heavy rain and storm

The Wuguang line and Guangshengang line mainly gocross such cities as Guangzhou Foshan and others inGuangzhou These cities are vulnerable to the typhoon fromcoastal regions which will affect the progress of the highspeed railway

The Yiwan line Suiyu line and Dacheng line go crossWanzhou Suining Shizishan Chengdu or other cities ofSichuan province High speed railway in these areas willsuffer seriously from the tough environment and we shouldpay attention to prevent cost and loss from landslip andearthquake


Xinjiang

Tibet

Gansu

Qinghai

Sichuan

Yunnan

Inner Mongolia

Ningxia Shanxi

Shaanxi Henan

Chong

Guizhou

Hubei

Guangxi Guangdong

FujianJiangxi

AnhuiShanghai

Zhejiang

Liaoning

Heilongjiang

Serious regionsMedium regionsSlight regions

Hunan

Shandong

Hebei

Tianjin

Jilin

Beijing

Qing

Jiangsu

Figure 2 Chinese environment impacts of high speed railway distribution

Table 8 The attribute recognition of high speed railway classification score

Classification No effect Slight Medium Serious Particularly seriousScore 90 80 70 60 50

5 Conclusions

Firstly the papermakes a detailed analysis of the impact fromsuch environment factors as rainfall earthquake lightningwind and snow on the high speed railway safety mechanismOn the basis of the analysis the evaluation index systemof safety has been established and the threshold of highspeed railway environmental safety has been calibrated byciting the results of domestic and abroad At last the highspeed railway uncertain safety attribute recognition model iscreated based on the Mahalanobis distance with the featuresof dimensionless andweak effect correlation which simplifiesthe comprehensive calculation process

Secondly the examples of Chinarsquos 31 provinces andregions in the paper are selected to make the data of the highspeed railway environmental safety much more convincingThe degree of danger is divided into five categories amongwhich the cities that the high speed railways pass in theserious category account for 161 those in the middle classaccount for 387 those in the mild category account for

387 and those in the no effect category account for 651It deserves our attention that cities of Xinjiang SichuanGuangdong Heilongjiang and Liaoning belong to the seri-ous category whose evaluation results are basically consistentwith the environmental characteristics And the results havea certain theoretical reference for the ldquo135rdquo planning of highspeed railway operation safety in Xinjiang and other areas

At last the analysis of the high speed railway environmen-tal safety is directed to the aspect of weather geology andother factors However considering the complexity of dataacquisition the high speed railway evaluation index has itsown drawbacks in this paper It is needed to introduce moremethods and factors into the evaluation of the high speedrailway safety operation to facilitate the further researches

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper


Table 9 The environment impacts of high speed railway lines distribution

Lines Serious environmental impact Middle environment impact Light environmental impactJinhu mdash Nanjing Jinan Beijing Tianjin and ShanghaiHebang mdash Hefei Bengbu mdashJinshi mdash Shijiazhuang BeijingWuguang mdash Wuhan Changsha Guangzhou FoshanGuangshengang mdash mdash Guangzhou ShenzhenYongtaiwen mdash mdash Ningbo Taizhou and WenzhouWenfu mdash Wenzhou Fuzhou mdashFuxia mdash Fuzhou Xiamen and Quanzhou mdashZhenxi mdash Zhengzhou Xi rsquoanXibao mdash mdash Xi rsquoan BaojiHuhang mdash Jiaxing Hangzhou ShanghaiHewu mdash Nanjing Hefei and Wuhan mdashHanyi mdash Hankou Zhijiang and Yinchuan mdashHening mdash Hefei Nanjing mdashJiaoji mdash mdash Jinan QingdaoShitai mdash mdash Shijiazhuang TaiyuanHuning mdash Nanjing Suzhou ShanghaiYiwan Wangzhou Ensi mdash YichangYuli mdash Chongqing Fuling LiangwuSuiyu Suining Shizuishan mdash mdashDacheng Dazhou Suining and Chengdu mdash mdash

Acknowledgments

The authors are very grateful to the anonymous referees fortheir insightful and constructive comments and suggestionsthat have led to an improved version of this paper The workalso was supported by National Nature Science Funding ofChina (Project no 51178157) The Basic Scientific ResearchBusiness Special Fund Project in Colleges and Universi-ties (no 2011zdjh29) National Statistical Scientific ResearchProjects (no 2012LY150) ldquoBlue Projectrdquo Projects in JiangsuProvince Colleges and Universities (no 201211) and YouthFund Projects in Jiangxi Province Department of Education(no GJJ13314)

References

[1] L Sun H Zhong and G Lin ldquoAn overview of earthquakeearly warning systems for high speed railway and its applicationto Beijing-Shanghai high speed railwayrdquo World EarthquakeEngineering vol 27 no 3 pp 14ndash16 2011

[2] L Wang Y Qin J Xu and L Jia ldquoA fuzzy optimizationmodel for high-speed railway timetable reschedulingrdquo DiscreteDynamics in Nature and Society vol 2012 Article ID 827073 22pages 2012

[3] H Tao N Hong-Xia and F Duo-Wang ldquoSpeed contronfor high-speed railway on multi-mode intelligent control andfeature recognitionrdquo Telkomnika no 8 pp 2069ndash2074 2012

[4] X Xiao L Ling and X Jin ldquoA study of the derailmentmechanism of a high speed train due to an earthquakerdquo VehicleSystem Dynamics vol 50 no 3 pp 449ndash470 2012

[5] J Calle-Sanchez M Molina-Garcıa J I Alonso and AFernandez-Duran ldquoLong term evolution in high speed railway

environments feasibility and challengesrdquo Bell Labs TechnicalJournal vol 18 no 2 pp 237ndash253 2013

[6] H Wang W Xu F Wang and C Jia ldquoA cloud-computing-based data placement strategy in high-speed railwayrdquo DiscreteDynamics in Nature and Society vol 2012 Article ID 396387 15pages 2012

[7] C Miyoshi and M Givoni ldquoThe environmental case for thehigh-speed train in theUK examining the London-Manchesterrouterdquo International Journal of Sustainable Transportation vol8 no 2 pp 107ndash126 2014

[8] L Zhou and Z Shen ldquoProgress in high-speed train technologyaround theworldrdquo Journal ofModern Transportation vol 19 no1 pp 1ndash6 2011

[9] L Ling X Xiao and X Jin ldquoStudy on derailment mechanismand safety operation area of high-speed trains under earth-quakerdquo Journal of Computational and Nonlinear Dynamics vol7 no 4 Article ID 041001 2012

[10] K S Lee J K Eom J Lee et al ldquoThe preliminary analysis ofintroducing 500 kmh high-speed rail in Koreardquo InternationalJournal of Railway vol 6 no 1 pp 26ndash31 2013

[11] W Zhang and J Zeng ldquoA review of vehicle system dynamics inthe development of high-speed trains in Chinardquo InternationalJournal of Dynamics and Control vol 1 no 1 pp 81ndash97 2013

[12] Q-Z Hu H-P Lu and W Deng ldquoEvaluating the urban publictransit network based on the attribute recognition modelrdquoTransport vol 25 no 3 pp 300ndash306 2010

[13] M Yan ldquoMultigranulations rough set method of attributereduction in information systems based on evidence theoryrdquoJournal of Applied Mathematics vol 2014 Article ID 857186 9pages 2014

[14] Z Xiao W Chen and L Li ldquoA method based on interval-valued fuzzy soft set for multi-attribute group decision-making


problems under uncertain environmentrdquo Knowledge and Infor-mation Systems vol 34 no 3 pp 653ndash669 2013

[15] R Todeschini D Ballabio V Consonni F Sahigara and PFilzmoser ldquoLocally centred Mahalanobis distance a new dis-tance measure with salient features towards outlier detectionrdquoAnalytica Chimica Acta vol 787 pp 1ndash9 2013

[16] N Kayaalp and G Arslan ldquoA fuzzy bayesian classifier withlearned mahalanobis distancerdquo International Journal of Intelli-gent Systems vol 29 no 8 pp 713ndash726 2014

[17] httpcdccmagovcnhomedo

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Distributed Sensor Networks


Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014


ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014


Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications


Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia


Biomedical Imaging


ArtificialNeural Systems

Advances in


RoboticsJournal of



Computational Intelligence and Neuroscience

Industrial EngineeringJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in



Table 1 High speed railway mechanism analysis of environmental impact factors

Environmentalfactor Mechanism

Rainfall

(i) Raining is the foremost factor that is easily causing line fault Additionally the current flow will emerge betweenthe pantograph and overhead line systems of the high speed railway when it comes to a heavy rainy day and the trainpower supply will also be consequently influenced(ii) Rainfall can cause mountain soil landslides that will directly lead to the abnormal operation of the train

Cross wind

(i) The mechanism of the influence caused by horizontal wind on the high speed railway is that it can produce theyawing force which will allow the lateral migration Moreover the produced lift force will lead to the trainderailment through the pneumatic action with the high speed train which will undeniably increase the risk of trainbeing derailed

Lightning (i) Lightning can disrupt the power supply of the high speed railway train traction which will result in the suddenstop of the high speed rail train through breaking down the high speed railway along the circuit devices

Earthquake(i) Earthquake wave can be divided into two kinds the P wave (primary wave pressure wave) and the S wave(secondary wave shear wave) S wave can destroy the building structure and cause the landslides orbital shift andtrain wheel derailment which will influence the safe driving of high speed railway

Temperature

(i) High temperature can lead to a big temperature difference between the internal and external the increase of airconditioning power and the aggravation of the train power supply load Besides high temperature can cause theshort circuit because of the softened line(ii) Cold damp climate will lead to the ice covering membrane on the railway track which will reduce the frictionbetween train and track and increase the risk of a derailment when the train is at high speed turning

Snowfall (i) A lot of snow will cover the track and the ice on the track will increase the degree of danger of the train operation

contend that all the systems can be still well improved on thebasis of the original ordinary railway disaster warning system[11] because there is a certain gap between foreign andChinarsquoshigh speed railway disaster warning systems after a relativelyfair comparison

Through the comparison of present researches betweendomestic and foreign we can find that the domestic highspeed railway disaster prevention is now in a transition fromtheory to practice while foreign high speed railway disasterprevention system has been at a relatively perfect stageTherefore it is an urgentmission for the domestic researchersto make an intensive effort to the theory research of highspeed railway disaster protection and system constructionprocess so as to promote China high speed railway operatingsafety level

2 High Speed Railway Environmental ImpactEvaluation Indexes

21 High Speed Railway Index System of EnvironmentalImpacts The operational problems of the high speed railwayare mainly caused by such uncertain factors as raining thun-dering and lightning horizontal wind earthquake and soforth whose degree of intensitywill directly decide the degreeof danger posing to the high speed railway operation safetyThe analysis of the characteristics of various environmentalfactors in the process of high speed railway operation inrecent years and the conclusion of themechanism of differentenvironmental factors on high speed railway safe operationare presented in Table 1

Besides the six factors listed in Table 1 problems in thehigh speed railway are also being influenced by debris flowand water and rock burst However given the complexity ofgeological conditions and the difficulty of data acquisition we

only use average annual rainfall average annual maximumlightning density annual disaster monsoon winds averagedisasters incidence of monsoon average magnitude gradeaverage incidence of earthquakes average annual maximumsnow depth average highest temperature and average min-imum temperature as the environment factor evaluationindex which are shown in Figure 1

It is necessary to bementioned that the usual climate envi-ronment will not exert any influence upon the operation ofhigh speed railway except typhoon sandstorm blizzard andearthquakes while high or low temperatures have significantinfluence on the operation of high speed railway Thereforewith the exclusive of the average rainfall in Figure 1 otherfactors represent the extreme climate environment Eachenvironmental factor evaluation index calculation formulaand specification is shown in the following equations

Average annual rainfall level is

AAR =119873

sum

119894=1

RF119894

119873

(1)

where RF119894is the maximum rainfall in the 119894th year (mm) and

is the number of the yearsMaximum lightning density is

MLD =

119873

sum

119895=1

LH119895

(119873 times area) (2)

where LH119895is the thunder lightning happening in certain

region in the 119895th year (time) and area is the area of a city orregion (m2)

Disasters wind speed is

AWS =119882119899

sum

119896=1

DWS119896

119882119899

(3)




Aver

age w

ind

spee

d

Aver

age s

now

dep

th

Aver

age m

agni

tude

gra

de

Aver

age l

ight

ning

den

sity

Aver

age a

nnua

l rai

nfal

l

Aver

age h

igh

tem

pera

ture

Aver

age l

ow te

mpe

ratu

re

Aver

age w

ind

happ

enin

g

Aver

age m

agni

tude

hap

peni






AWH =

119882119899

119873

(4)



AMG =

119863119899

sum

119897=1

MG119897

119863119899

(5)




AWH =

119863119899

119873

(6)


AHT =119873

sum

119901=1

Max119879119901

119873

ALT =119873

sum

119901=1

Min119879119901

119873

(7)




ASD =

119873

sum

119903=1

Max SD119903

119873

(8)





EAT = 119860119863

(9)


















1199091 1199092 1199093 119909









1198681

1198682

1198689

11986211198622119862311986241198625

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

1198861111988612119886131198861411988615

1198862111988622119886231198862411988625

1198869111988692119886931198869411988695

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

(10)

where 119886119894119895(119894 = 1 2 9 119895 = 1 2 3 4 5) 119886

1198941gt 1198861198942gt 1198861198943gt

1198861198944gt 1198861198945



119894119896= 119906 (119906

119894isin






1198622

1198623

1198624

1198625

















119896is

119889119894119896=radic(119883119894minus 119862119896) Σ

minus1

119894119896(119883119894minus 119862119896)

119879

(11)

where 119883119894= (1199091198941 1199091198942 119909



= (1198861198961

1198861198962 119886




119896is

Σ119894119896=

[

[

[

[

Cov (1199091198941 1198861198961) Cov (119909


1198941 1198861198969)

Cov (1199091198942 1198861198961) Cov (119909


1198942 1198861198969)


Cov (1199091198949 1198861198961) Cov (119909


1198949 1198861198969)

]

]

]

]

(12)




119896


value is

119906119894119896=

1119889119894119896

sum

5

119895=1(1119889119894119895)

(13)


If 119896119894= min119896

119896

sum

119897=1

119906119894119897ge 120582 119896 = 5 4 3 2 1


119896

(14)




119896 then the


119878119894=

4

sum

119896=1

119906119894119896119902119896 (15)

4 Case Studies




Region Index1198831

1198832

1198833

1198834

1198835

1198836

1198837

1198838

1198839






that is

1198775times9

=

[

[

[

[

[

[

[

[

[

[

[

[

[

350 325 275 225 100

300 250 150 075 025

300 260 195 130 450

520 500 460 415 195

100 080 045 020 005

550 500 400 225 750

2970 2475 1440 750 300

640 560 415 300 175


]

]

]

]

]

]

]

]

]

]

]

]

]

(16)


119911 = pdist (11987731times9








Xinjiang 0301 0402 0117 0003 0177


Medium


Slight











Xinjiang

Tibet

Gansu

Qinghai

Sichuan

Yunnan

Inner Mongolia

Ningxia Shanxi

Shaanxi Henan

Chong

Guizhou

Hubei

Guangxi Guangdong

FujianJiangxi

AnhuiShanghai

Zhejiang

Liaoning

Heilongjiang


Hunan

Shandong

Hebei

Tianjin

Jilin

Beijing

Qing

Jiangsu




5 Conclusions










Acknowledgments


References




























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






Aver

age w

ind

spee

d

Aver

age s

now

dep

th

Aver

age m

agni

tude

gra

de

Aver

age l

ight

ning

den

sity

Aver

age a

nnua

l rai

nfal

l

Aver

age h

igh

tem

pera

ture

Aver

age l

ow te

mpe

ratu

re

Aver

age w

ind

happ

enin

g

Aver

age m

agni

tude

hap

peni






AWH =

119882119899

119873

(4)



AMG =

119863119899

sum

119897=1

MG119897

119863119899

(5)




AWH =

119863119899

119873

(6)


AHT =119873

sum

119901=1

Max119879119901

119873

ALT =119873

sum

119901=1

Min119879119901

119873

(7)




ASD =

119873

sum

119903=1

Max SD119903

119873

(8)





EAT = 119860119863

(9)


















1199091 1199092 1199093 119909









1198681

1198682

1198689

11986211198622119862311986241198625

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

1198861111988612119886131198861411988615

1198862111988622119886231198862411988625

1198869111988692119886931198869411988695

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

(10)

where 119886119894119895(119894 = 1 2 9 119895 = 1 2 3 4 5) 119886

1198941gt 1198861198942gt 1198861198943gt

1198861198944gt 1198861198945



119894119896= 119906 (119906

119894isin






1198622

1198623

1198624

1198625

















119896is

119889119894119896=radic(119883119894minus 119862119896) Σ

minus1

119894119896(119883119894minus 119862119896)

119879

(11)

where 119883119894= (1199091198941 1199091198942 119909



= (1198861198961

1198861198962 119886




119896is

Σ119894119896=

[

[

[

[

Cov (1199091198941 1198861198961) Cov (119909


1198941 1198861198969)

Cov (1199091198942 1198861198961) Cov (119909


1198942 1198861198969)


Cov (1199091198949 1198861198961) Cov (119909


1198949 1198861198969)

]

]

]

]

(12)




119896


value is

119906119894119896=

1119889119894119896

sum

5

119895=1(1119889119894119895)

(13)


If 119896119894= min119896

119896

sum

119897=1

119906119894119897ge 120582 119896 = 5 4 3 2 1


119896

(14)




119896 then the


119878119894=

4

sum

119896=1

119906119894119896119902119896 (15)

4 Case Studies




Region Index1198831

1198832

1198833

1198834

1198835

1198836

1198837

1198838

1198839






that is

1198775times9

=

[

[

[

[

[

[

[

[

[

[

[

[

[

350 325 275 225 100

300 250 150 075 025

300 260 195 130 450

520 500 460 415 195

100 080 045 020 005

550 500 400 225 750

2970 2475 1440 750 300

640 560 415 300 175


]

]

]

]

]

]

]

]

]

]

]

]

]

(16)


119911 = pdist (11987731times9








Xinjiang 0301 0402 0117 0003 0177


Medium


Slight











Xinjiang

Tibet

Gansu

Qinghai

Sichuan

Yunnan

Inner Mongolia

Ningxia Shanxi

Shaanxi Henan

Chong

Guizhou

Hubei

Guangxi Guangdong

FujianJiangxi

AnhuiShanghai

Zhejiang

Liaoning

Heilongjiang


Hunan

Shandong

Hebei

Tianjin

Jilin

Beijing

Qing

Jiangsu




5 Conclusions










Acknowledgments


References




























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




















1199091 1199092 1199093 119909









1198681

1198682

1198689

11986211198622119862311986241198625

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

1198861111988612119886131198861411988615

1198862111988622119886231198862411988625

1198869111988692119886931198869411988695

100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816100381610038161003816

(10)

where 119886119894119895(119894 = 1 2 9 119895 = 1 2 3 4 5) 119886

1198941gt 1198861198942gt 1198861198943gt

1198861198944gt 1198861198945



119894119896= 119906 (119906

119894isin






1198622

1198623

1198624

1198625

















119896is

119889119894119896=radic(119883119894minus 119862119896) Σ

minus1

119894119896(119883119894minus 119862119896)

119879

(11)

where 119883119894= (1199091198941 1199091198942 119909



= (1198861198961

1198861198962 119886




119896is

Σ119894119896=

[

[

[

[

Cov (1199091198941 1198861198961) Cov (119909


1198941 1198861198969)

Cov (1199091198942 1198861198961) Cov (119909


1198942 1198861198969)


Cov (1199091198949 1198861198961) Cov (119909


1198949 1198861198969)

]

]

]

]

(12)




119896


value is

119906119894119896=

1119889119894119896

sum

5

119895=1(1119889119894119895)

(13)


If 119896119894= min119896

119896

sum

119897=1

119906119894119897ge 120582 119896 = 5 4 3 2 1


119896

(14)




119896 then the


119878119894=

4

sum

119896=1

119906119894119896119902119896 (15)

4 Case Studies




Region Index1198831

1198832

1198833

1198834

1198835

1198836

1198837

1198838

1198839






that is

1198775times9

=

[

[

[

[

[

[

[

[

[

[

[

[

[

350 325 275 225 100

300 250 150 075 025

300 260 195 130 450

520 500 460 415 195

100 080 045 020 005

550 500 400 225 750

2970 2475 1440 750 300

640 560 415 300 175


]

]

]

]

]

]

]

]

]

]

]

]

]

(16)


119911 = pdist (11987731times9








Xinjiang 0301 0402 0117 0003 0177


Medium


Slight











Xinjiang

Tibet

Gansu

Qinghai

Sichuan

Yunnan

Inner Mongolia

Ningxia Shanxi

Shaanxi Henan

Chong

Guizhou

Hubei

Guangxi Guangdong

FujianJiangxi

AnhuiShanghai

Zhejiang

Liaoning

Heilongjiang


Hunan

Shandong

Hebei

Tianjin

Jilin

Beijing

Qing

Jiangsu




5 Conclusions










Acknowledgments


References




























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






1198622

1198623

1198624

1198625

















119896is

119889119894119896=radic(119883119894minus 119862119896) Σ

minus1

119894119896(119883119894minus 119862119896)

119879

(11)

where 119883119894= (1199091198941 1199091198942 119909



= (1198861198961

1198861198962 119886




119896is

Σ119894119896=

[

[

[

[

Cov (1199091198941 1198861198961) Cov (119909


1198941 1198861198969)

Cov (1199091198942 1198861198961) Cov (119909


1198942 1198861198969)


Cov (1199091198949 1198861198961) Cov (119909


1198949 1198861198969)

]

]

]

]

(12)




119896


value is

119906119894119896=

1119889119894119896

sum

5

119895=1(1119889119894119895)

(13)


If 119896119894= min119896

119896

sum

119897=1

119906119894119897ge 120582 119896 = 5 4 3 2 1


119896

(14)




119896 then the


119878119894=

4

sum

119896=1

119906119894119896119902119896 (15)

4 Case Studies




Region Index1198831

1198832

1198833

1198834

1198835

1198836

1198837

1198838

1198839






that is

1198775times9

=

[

[

[

[

[

[

[

[

[

[

[

[

[

350 325 275 225 100

300 250 150 075 025

300 260 195 130 450

520 500 460 415 195

100 080 045 020 005

550 500 400 225 750

2970 2475 1440 750 300

640 560 415 300 175


]

]

]

]

]

]

]

]

]

]

]

]

]

(16)


119911 = pdist (11987731times9








Xinjiang 0301 0402 0117 0003 0177


Medium


Slight











Xinjiang

Tibet

Gansu

Qinghai

Sichuan

Yunnan

Inner Mongolia

Ningxia Shanxi

Shaanxi Henan

Chong

Guizhou

Hubei

Guangxi Guangdong

FujianJiangxi

AnhuiShanghai

Zhejiang

Liaoning

Heilongjiang


Hunan

Shandong

Hebei

Tianjin

Jilin

Beijing

Qing

Jiangsu




5 Conclusions










Acknowledgments


References




























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





Region Index1198831

1198832

1198833

1198834

1198835

1198836

1198837

1198838

1198839






that is

1198775times9

=

[

[

[

[

[

[

[

[

[

[

[

[

[

350 325 275 225 100

300 250 150 075 025

300 260 195 130 450

520 500 460 415 195

100 080 045 020 005

550 500 400 225 750

2970 2475 1440 750 300

640 560 415 300 175


]

]

]

]

]

]

]

]

]

]

]

]

]

(16)


119911 = pdist (11987731times9








Xinjiang 0301 0402 0117 0003 0177


Medium


Slight











Xinjiang

Tibet

Gansu

Qinghai

Sichuan

Yunnan

Inner Mongolia

Ningxia Shanxi

Shaanxi Henan

Chong

Guizhou

Hubei

Guangxi Guangdong

FujianJiangxi

AnhuiShanghai

Zhejiang

Liaoning

Heilongjiang


Hunan

Shandong

Hebei

Tianjin

Jilin

Beijing

Qing

Jiangsu




5 Conclusions










Acknowledgments


References




























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






Xinjiang 0301 0402 0117 0003 0177


Medium


Slight











Xinjiang

Tibet

Gansu

Qinghai

Sichuan

Yunnan

Inner Mongolia

Ningxia Shanxi

Shaanxi Henan

Chong

Guizhou

Hubei

Guangxi Guangdong

FujianJiangxi

AnhuiShanghai

Zhejiang

Liaoning

Heilongjiang


Hunan

Shandong

Hebei

Tianjin

Jilin

Beijing

Qing

Jiangsu




5 Conclusions










Acknowledgments


References




























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




Xinjiang

Tibet

Gansu

Qinghai

Sichuan

Yunnan

Inner Mongolia

Ningxia Shanxi

Shaanxi Henan

Chong

Guizhou

Hubei

Guangxi Guangdong

FujianJiangxi

AnhuiShanghai

Zhejiang

Liaoning

Heilongjiang


Hunan

Shandong

Hebei

Tianjin

Jilin

Beijing

Qing

Jiangsu




5 Conclusions










Acknowledgments


References




























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






Acknowledgments


References




























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in















Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



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