Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (6): 1861-1872.doi: 10.13229/j.cnki.jdxbgxb.20221395

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Monitoring road geological disaster based on satellite remote sensing

Jun-qing ZHU1(),Xue-ru ZHAO1,Tao MA1(),Xiao-ming HUANG1,Hong-zhou ZHU2   

  1. 1.School of Transportation,Southeast University,Nanjing 211102,China
    2.School of Civil Engineering,Chongqing Jiaotong University,Chongqing 400074,China
  • Received:2022-11-01 Online:2023-06-01 Published:2023-07-23
  • Contact: Tao MA E-mail:zhujq@seu.edu.cn;matao@seu.edu.cn

Abstract:

Defining highway region as study area, the research subject of this paper was constructing an early warning monitoring method system for geological disaster. Combined with the characteristics of highway, eight disaster-causing factors were selected:elevation, topographic slope, surface deformation rate, rainfall, land-use, engineering geological rock formations, fault distribution and waterway distribution. Information extraction was solved by satellite remote sensing technology to input to the weights of evidence and logistic regression coupled model, in order to get expressway within the scope of the geological hazards in the calculation results. The results were divided into regions to determine the target region of early warning and monitoring. The practice subject of this method is the highway from Ya'an to Kangding in Sichuan Province. The results show that this method has a good data representation effect on the characteristics of the road research area with highway as the main body. The accuracy of early warning and monitoring of geological disasters in the road region is high, and the AUC value is 0.906. This method has certain reference and guiding significance for disaster prevention and resistance of highway traffic infrastructure.

Key words: road and railway engineering, satellite remote sensing, geological hazard, early warning and monitoring, highway region, disaster susceptibility

CLC Number: 

  • U416

Fig.1

Flowchart of WOE-LR coupling model"

Fig.2

Location and geological hazard distribution map of study area"

Fig.3

Distribution map of geological hazard factors in Ya-kang Highway"

Fig.4

Schematic diagram of calculation raster of slope"

Table 1

Results of hazard factors index calculated by WOE"

致灾因子指标分级Wi+Wi-Wfi致灾因子指标分级Wi+Wi-Wfi
地面高程/m0~10351.4416-1.35212.7937降水/(mm·年-1945-1.34210.1166-1.4587
1035~1455-0.27000.0400-0.3100970-1.44770.1126-1.5603
1455~1855-1.40120.1268-1.52801000-1.29580.1373-1.4331
1855~2265-1.95770.1338-2.09151020-1.99550.1402-2.1357
2265~2705-1.47530.0941-1.56941045-0.69620.0850-0.7812
2705~3200-2.45440.0974-2.551810701.1107-0.38161.4923
3200~3760-2.53250.0695-2.602011001.1306-0.34771.4783
3760~58900.00000.00000.0000

土地使用

类型

水体1.0773-0.02331.1007
地形坡度/(°)0~51.1727-0.07231.2450植被-0.21300.4737-0.6867
5~100.9871-0.12561.1127湿地1.1186-0.00191.1205
10~150.9560-0.17181.1278耕地1.0235-0.02461.0481
15~200.4347-0.06970.5045牧场-0.94680.0847-1.0315
20~250.1690-0.02760.1966建筑1.6135-0.24391.8574
25~30-1.15440.1109-1.2654裸地-1.55490.0219-1.5768
30~35-1.02040.1085-1.1289雪/冰0.00000.00000.0000
35~40-1.08800.0931-1.1811断层分布/m0~8900.2049-0.06560.2706
40~45-1.56090.0685-1.6294890~18660.1639-0.04220.2061
45~50-2.65390.0389-2.69291866~29250.2456-0.04990.2955
50~55-1.09270.0116-1.10422925~4076-0.38420.0453-0.4295
55~750.00000.00000.00004076~5346-0.31010.0299-0.3400
地表形变速率/(mm·年-1-27.99~-9.690.00000.00000.00005346~6788-0.01320.0012-0.0143
-9.69~-5.78-0.90850.0594-0.96796788~8457-0.41420.0169-0.4311
-5.78~-2.61-0.11440.0231-0.13758457~10203-0.58370.0213-0.6050
-2.61~0.55-0.02290.0054-0.028310203~12690-0.73770.0227-0.7604
0.55~3.730.6971-0.26290.9601水系分布/m0~8170.9392-0.46911.4083
3.73~7.140.1789-0.03430.2132817~16980.1164-0.02550.1419
7.14~11.29-0.65630.0584-0.71471698~2621-0.21620.0347-0.2509
11.29~34.24-1.96220.0361-1.99832621~3598-0.29220.0401-0.3322
工程地质岩组侵入岩组-1.43290.2044-1.63733598~4688-0.33020.0349-0.3651
熔岩组-0.96670.0820-1.04884688~5897-2.74610.0883-2.8344
变质岩组-0.06740.0181-0.08555897~120420.00000.00000.0000
碳酸盐岩-1.16490.1206-1.2856
碎屑盐岩0.6075-0.34220.9497
松散堆积层2.2154-0.17082.3862

Table 2

Correlation matrix of hazard factors"

Xx1x2x3x4x5x6x7x8
x11.000-------
x20.2411.000------
x30.011-0.0171.000-----
x40.1960.167-0.1051.000----
x50.2850.114-0.0310.2151.000---
x60.2130.167-0.0370.2910.2591.000--
x70.1530.113-0.0210.1720.0570.0641.000-
x80.1970.039-0.1330.1850.1030.2450.1781.000

Table 3

Weights and parameters of logistic regression"

变量BSEwalsdfsig
地面高程3.9771.4427.60210.006
地形坡度1.6560.32725.63310.001
地表形变速率0.8950.3616.12710.013
工程地质岩组1.4080.37713.95710.001
降水0.3470.3014.33210.028
土地使用类型0.3870.4545.72510.015
断层分布0.8120.2977.46810.006
水系分布1.8840.27546.95910.001
常量-3.4430.38181.55510.000

Fig.5

Schematic diagram of WOE-LR model stacking process"

Fig.6

Target regionalization result of WOE-LR"

Fig.7

Target regionalization result of WOE"

Table 4

Susceptibility zoning statistics of WOE-LR"

区划分级面积/km2面积占比/%灾害点灾害占比/%
总计2662.06100347100
不易发生1590.4659.74226.34
较少发生573.8521.563710.66
易发生367.9513.8213037.47
极易发生129.804.8815845.53

Table 5

Susceptibility zoning statistics of WOE"

区划分级面积/km2面积占比/%灾害点灾害占比/%
总计2662.06100347100
不易发生680.2525.55257.20
较少发生794.7529.864813.84
易发生506.8119.0416948.70
极易发生680.2525.5510530.26

Fig.8

ROC curve of road region monitored by geological hazard susceptibility"

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