Application of ARCGIS and Analytical Hierarchy Process in the Risk Assessment of Geological Disaster in Complex Mountains

Transcription

1 Application of ARCGIS and Analytical Hierarchy Process in the Risk Assessment of Geological Disaster in Complex Mountains Li Li Lecturer Key Laboratory of Water Environment Evolution and Pollution Control in Three Gorges Reservoir, Chongqing Three Gorges University, Wanzhou District of Chongqing 40400, China Dou Jing-xiang Engineer Ma an Shan Surveying and Mapping Technology Institute, Anhui province43000,china Qiang Yue, Associate Professor National and Local Joint Engineering Laboratory of Transportation and Civil Engineering Materials, Chongqing Jiaotong University, Chongqing , China Collegeof Civil Engineering, Chongqing Three Gorges University, Chongqing 40400, China ABSTRACT Based on the precipitating factors of geological disasters in complex mountainous regions, this paper took geological disaster distribution, seismic intensity, fracture, engineering petrofabric, river distance, slope and precipitation as the risk indicators of geological disasters. Next, a mathematical model was established by using the analytical hierarchy process. The weight of above seven risk indicators and the coincidence indicator were determined by MATLAB programming which has satisfying calculation accuracy and reliability. Finally, the ARCGISbased spatial calculation and analysis disclosed the risk assessment result of geological disasters in the research area. The results demonstrated good effect of the evaluation model and programming based on the combination of analytical hierarchy process and ARCGIS in the risk assessment of geological disasters in complex mountainous region. This can provide some references to similar assessment. KEYWORDS: Geological disaster, earthquake, Three Gorges Dam, Risk Analysis INTRODUCTION Geological disaster is caused by natural or man-made geological process and can bring severe damages or even catastrophic damages to human life, materials and the ecological environment, including violent earthquake, avalanche, landslide, mud-rock flow, ground fissure, water and soil loss, land desertification and paludification, etc. China is a country suffering frequent geological disasters. With the worsening ecological environment and intensifying human activities, China,

2 Vol. 9 [04], Bund. G 370 especially in complex mountainous regions, is suffering more and more serious geological disasters like landslide, mud-rock flow, water and soil loss, etc., which not only threaten people s life and property, but also influence the development of social sciences []. Recently, associated discussions and researches have been conducted by experts and scholars. Tang Chuan analyzed the geological disasters in the Jin-Sha River Valley in Yunnan Province []. Wang Zhe et al. evaluated the susceptibility to geological disasters in Mian-Yang by using single analytic hierarchy process [3]. Meng Qing-hua et al. studied the susceptibility to geological disasters in the Feng Xian County, Shanxi Province by combining the GIS technology and information value method [4]. Based on the GIS and contribution weight model of background factors, Wang Meng et al. evaluated the landslide risks in Wan-Zhou, Chongqing [5]. These literatures, using quantitative or qualitative method to discuss and evaluate the susceptibility and risks of geological disasters, further facilitate the development of geological disaster researches. Deep assessment of geological disasters (e.g. avalanche, landslide and mud-rock flow) in complex mountainous regions with big topographic relief and complex geological conditions requires cooperation of quantitative and qualitative methods as well as a mathematical model. Based on the Ping-Wu County, a complex mountainous region suffering hard-hit during the Wen-Chuan Earthquake, this paper established seven risk indicators of geological disasters and established a mathematical model by using the analytical hierarchy process. The weight of these seven risk indicators and the coincidence indicator in the model were calculated by the MATLAB programming. Finally, risks of geological disasters in the research area were graded and evaluated through the ARCGIS-based spatial calculation and analysis. INTRODUCTION TO THE RESEARCH AREA Pingwu County is characteristic of great changing landform and complicated tectonics, manifested by 94.33% mountain lands above 000m altitude and 67.3% slopes larger than 5. The Longmenshan tectonic belt, known as the meizoseismal area of the Wenchuan Earthquake on May,, 008, is widely distributed in Pingwu County. Types, amount and scale of geological disasters recorded in the Report of Geological Disasters in Pingwu County, Mianyang City, Sichuan Province during the Wenchuan Earthquake on May,, 008 are listed in Table. Table : Geological disaster and size type table of Pingwu county Disasters types Level indicators Super-huge Huge Medium Small Summation Landslide Debris flow Collapse grading standard (04m3) <0 quantity(entries) proportion (%) grading standard (04m3) > < quantity(entries) 3 5 Proportion (%) grading standard (04m3) > < quantity(entries) proportion(%) Unstable slope grading standard (04m3) > <0 quantity(entries) proportion(%)

3 Vol. 9 [04], Bund. G 37 RESEARCH PRINCIPLES AND METHOD Analytical Hierarchy Process and Implementation Analytical hierarchy process was put forward at the beginning of 970s. It is a decisionmaking analysis combining the qualitative analysis and quantitative calculation. Firstly, it decomposes the complicated problem into hierarchical structures. Secondly, experienced experts will judge these hierarchical structures and establish a judgment matrix. The calculated results of the judgment matrix will receive further hierarchical sorting and consistency check. Finally, weight of indicators passing the consistency check will be determined. To promise the calculation accuracy and reliability of weight and coincidence indicator, this paper applied the MATLAB software. The MATLAB software has powerful calculation capabilities and is superior in matrix calculation. Matrix eigenvalue and eigenvector can be calculated easily by the eig function ([V, D]=eig(A)). To avoid complex numbers, V and D were judged properly by using the maxeigvalvec.m function. Considering the matrix calculation of MATLAB and characteristics of analytical hierarchy process, the consistency of hierarchy single sorting was checked through the sglsortexamine.m function, while the overall ranking weight and consistency check of the analytical hierarchy process were accomplished through the tolsortvec.m function [6]. According to the analysis, the block diagram of calculation program can be gained (Fig.). Start Insert B B,C factors structure criterion Form a judgment matrix Hierarchical analysis single order Solve the consistency index Cr N Insert Amax eigenvector ω CI CR Total sorts consistency judgment CR<0.? Y End Consistency judgment CR<0.? N Y Total sorts Figure: The table of weight vector calculation program

4 Vol. 9 [04], Bund. G 37 GIS-based information computing and analysis Information value method calculates the information value provided by influencing factors as the quantitative indicator for zoning. This not only can reflect the basic law of geological disasters, but also is simple, easy-to-operation, practical and convenient for popularization. Information value of basic indicators influencing the risks of geological disasters can be calculated by the ARCGIS-based calculation and spatial analysis, which can be served as the quantitative indicators of risk assessments. Data process flow Influencing risk evaluation indicators were selected according to the basic geographic data and basic geological data. Weight of these evaluation indicators was determined by analytical hierarchy process. ARCGIS software was used for gridding and information value calculation of evaluation indicators. Finally, a spatial overlay analysis of the information value and weight of indicators was implemented based on the spatial analysis of ARCGIS to disclose the risk distribution of geological disasters in the research area. RISK ASSESSMENT BASED ON GIS AND ANALYTICAL HIERARCHY PROCESS Risk evaluation indicators of geological disasters in complex mountains Geological disaster risk refers to the possibility of geological disasters [7]. To highlight the preconditions and major induction factors of geological disasters in complex mountainous region in the research area, geological disaster distribution, seismic intensity, fracture, engineering petrofabric, river distance, slope and precipitation were taken as the risk evaluation indicators. Among them, the first four are the basic geological conditions of geological disasters in complex mountainous region, while the rest three are the major induction factors. In the research area, the seismic intensity covers V, VI, VII and VIII levels. The quantitative classification of risks of seismic intensity is shown in Fig.(a). Due to the complicated tectonics and steep terrain in the research area, rock stratum offsets through the physical and mechanical destructions caused by stress, thus causing geological disasters, such as landslide, avalanche, mud-rock flow, etc. These geological disasters are divided into four properties (>000m, m, m and <500m) according to their distance to the fault zone. The quantitative classification of risks of fracture is shown in Fig.(b). According to the engineering geological conditions of different rock types, the engineering petrofabric in Pingwu County is divided into medium-thick stratiform sedimentary rock, medium-thick stratiform metamorphic rock, thin stratiform sedimentary rock and fragmented volcanic as well as thin stratiform metamorphic rock. The quantitative classification of risks of engineering petrofabric is shown in Fig.(c). River erosion is graded according to river s distance to the established river buffer region. The further away to rivers, the smaller the geological disaster risk, otherwise, the larger the geological disaster risk (Fig.(d)). Researches demonstrated that ground near slopes will suffer stronger gravitational pull as the slope increases, which implies higher possibility of geological disasters. According to the geological survey of 63 landslides and unstable slopes in Pingwu County in 008, most slopes are larger than 5 ; most landslides are happened on slopes; slopes suffering avalanches are larger than 55 ; and the five mud-rock flows are produced in ravines with large brae. To analyze the geological disaster risks of complex mountainous regions with different slopes, slopes are divided into 0 ~8, 8 ~0, 0 ~50 and 50 ~5. The quantitative

5 Vol. 9 [04], Bund. G 373 classification of risks of slopes is shown in Fig.(e). Mud-rock flow, landslide and avalanche in Pingwu County are mainly occurred in the rainy season (May ~ September). To represent the impact of spatial distribution of precipitation on the geological disaster risk in complex mountainous region, this paper used the average annual precipitation as an evaluation indicator. Regions with more than,400mm annual precipitation are determined as high risk regions of geological disasters, while those with less than 800mm annual precipitation are determined as low risk regions. The quantitative classification of risks of precipitation is shown in Fig.(f). Distribution density of geological disasters can reflect the impact of geographical and geological conditions in the research area on the geological disasters. Larger distribution density of geological disasters indicates larger impact of geological disaster risk. In this paper, the distribution density of geological disasters is divided into four sections: >0.4 geological disaster /km (high risk), geological disaster /km (upper intermediate risk), geological disaster /km (medium risk) and <0.03 geological disaster /km (low risk). The quantitative classification of risks of disaster distribution density is shown in Fig.(g). Model for analytical hierarchy process Risk evaluation of geological disasters in Pingwu County took seismic intensity, fracture, engineering petrofabric, river erosion, slope, precipitation and geological disaster distribution as the risk indicators. To reflect such difference objectively, analytical hierarchy process was used to determine their weight. The structural model of the analytical hierarchy process is presented in Fig.3. (a) earthquake intensity risk classification figure (b) fracture risk classification figure (c) engineering petrofabric risk classification figure (d) river distance risk classification figure

6 Vol. 9 [04], Bund. G 374 (e) slope risk classification figure (f) rainfall risk classification figure (g) disaster density risk classification figure Figure : Geological disaster risk index classification figure Risk assessment of geological disasters(a) target layer Basic geographic data(b) Basic geo-logical data(b) criterion layer Geological disaster density(c) Slope (C) Rainfall (C3) River distance (C4) Earthquake intensity (C5) Fracture intensity (C6) Engineering petrofabric (C7) Figure 3: Hierarchical analysis structure model project layer

7 Vol. 9 [04], Bund. G 375 Construct judgment matrix The judgment matrix of target layer (A) to two criterion layers (B) is: ϖ A B = () The judgment matrix of criterion layer (B) to the project layer (C) is: ϖ B C / 3 = / 3 / , ϖ B C = / / () Table : Geologic disaster danger evaluation index weight and consistency check chart Evaluation index Weighted Approximate Factor weight sum λ C C C C C C C λmax CI C RI C CR C Result Accept (CR<0.) According to the calculated results, it can be reckoned that the weight vectors of the seven risk evaluation indicators of geological disasters in Pingwu County in relative to the total goal are: w =( w, w,, w 7 )=(.998, , , ,.6408, , ). Since the judgment matrix B to A is a second-order matrix (RI=0 and CR=0), CRB=0 and CRC= CR C = The consistency check of the whole hierarchical structural model of geological disaster risk in Pingwu County found that CR= CRB+ CRC=0.04<0., indicating the satisfying consistency. Risk evaluation result A weighted staking calculation and analysis was conducted in the ARCGIS by using the above calculated weights, which divided the geological disasters in complex mountainous regions in Pingwu County into four levels: low risk region, medium risk region, high risk region and higher risk region. The evaluation results are presented in Fig.4.

8 Vol. 9 [04], Bund. G 376 Figure 4: Geological disaster risk classification in Pingwu County CONCLUSION () Analytical hierarchy process is used for modeling and MATLAB programming is used for calculating the weight of risk indicators of geological disasters. The MATLAB programming not only has satisfying calculation accuracy and reliability, but also avoids using approximation method to calculate the maximum egienvalue and eigenvector of matrix, indicating its feasibility. () The analytical hierarchy process-based comprehensive risk assessment of geological disasters in complex mountainous regions in Pingwu County demonstrates that high risk regions are mainly in the middle and southeast regions where are mainly influenced by precipitation, slope and local engineering petrofabric. This indicated the significant role of geological disaster distribution, slope, precipitation and seismic intensity in the risk assessment of geological disasters. The rest risk indicators exert small impact on geological disasters in Pingwu County. (3) The risk assessment of geological disasters in complex mountainous regions based on analytical hierarchy process and ARCGIS agrees with practical situations and can be used to manage and prevent geological disasters. Meanwhile, the evaluation model and means are worth of popularization to certain extent and can provide references to similar risk assessment of geological disasters. ACKNOWLEDGMENTS This project is supported by Scientific and Technological Research Program of Chongqing Municipal Education Commission(Grant No. KJ307), Open Fund Program of Key Laboratory of Water Environment Evolution and Pollution Control in Three Gorges Reservoir of Chongqing Three Gorges University (Grant No. 0QN-07)and Young Scientific Program of Chongqing Three Gorges University (Grant No.QN7 ). REFERENCES. CH.S. Zhang, M.L. WU, Y.C. Zhang. Method and prospect of geological disaster risk assessment. Journal of Natural Disasters, Vol. (): 96-0(003).. CH. Tang. Assessment of Mountain Disasters in the Jinsha River Watershed of Yunnan. Journal of Mountain science, Vol. (4): (004).

9 Vol. 9 [04], Bund. G Z. Wang, F.CH.Yi. AHP-based evaluation of occurrence easiness of geological disasters in Mianyang City. Journal of Natural Disasters, Vol.8 (): 4-3(009). 4. Q. H. Meng, W.F.Sun, T.Wang. GIS Based Four Zones of Geohazard Susceptibility Degrees for Fengxian County in Qinling Mountains of Shaanxi Province. Journal of Engineering Geology, Vol.9 (3): (0). 5. M. Wang, J.P.Qiao, C.Y.Wu. Regional landslide danger assessments based on GIS and the root factor contributing weight model: a case study of the Wanzhou District, Chongqing City, China. Geological Bulletin of China, Vol.7 (): (008). 6. Y. Qiang, Y.X.He, G.H.Liu. Construction Risk Assessment of Small and Mediumsized Water Conservancy and Hydroelectric Power Engineering Based on Fuzzy Analytic Hierarchy Method. Construction Technology, Vol.4 (): 5-54(03). 7. Edy Tonnizam Mohamad, Danial Jahed Armaghani, Hossein Motaghedi. The Effect of Geological Structure and Powder Factor in Flyrock Accident, Masai, Johor, Malaysia. Electronic Journal of Geotechnical Engineering, Vol.8 (X): (03). 04, EJGE