Hazard Mapping by Frequency Ratio Approach using GIS

Transcription

1 Hazard Mapping by Frequency Ratio Approach using GIS Arzu ERENER 1, Suzanne LACASSE 2, Amir M. KAYNIA 3 Middle East Technical University, Ankara/TURKEY, erener@metu.edu.tr, on leave at International Centre for Geohazards, NGI, Oslo, Norway International Centre for Geohazards, NGI, Oslo, Norway, suzanne.lacasse@ngi.no International Centre for Geohazards, NGI, Oslo, Norway, amir.kaynia@ngi.no ABSTRACT Quick clay landslides represent a natural hazard to life, property and the environment in Norway. Reliable hazard and risk assessment approaches would help mitigate the consequences of such landslides. The paper presents a Geographical Information System (GIS) study of the sliding hazard presented by quick clay deposits in an area in southern Norway. The potential hazard area was analysed and mapped using the probabilistic frequency ratio approach. The factors considered in the study included slope angle, aspect, curvature and elevation derived from topographic databases, geological formation and soil type derived from geological maps and geotechnical soundings, distance to transportation infrastructure, rivers and lakes, and anthropogenic activities in the area. The results of the analysis were validated with comparisons with the actual locations of the quick clay slides inventoried at the site. Urbanization proves to be one of the most important triggers for quick clay slides in the region studied. In the probabilistic approach, frequency ratio values for each of these parameters were computed. With an overlay analysis, the frequency ratios were summed to yield a landslide hazard index and hazard map. The frequency ratio probabilistic approach was found to give realistic results. The analyses also showed that the Geographical Information System (GIS) technology provides a powerful tool to do spatial analysis and to model landslide hazard. Key Words: Hazard zonation, frequency ratio, quick clay 1. INTRODUCTION Landslides can occur frequently in quick clay areas in Norway. The landslides have caused disasters in historic time and represent threat in Norway every year, with potential loss of life, material losses, and interruption of communication, including transportation. The losses can be reduced by systematic assessment and management of landslide risk. Risk is defined as the measure of the probability and severity of an adverse effect to life, health, environment, property or reputation. Quantitatively, risk is the product of the hazard times the potential worth of loss. Since hazard assessment is one component of landslide risk, it should be properly assessed. Susceptibility or hazard maps are one of the most important stages in landslide hazard mitigation. The use of these maps can reduce loss of life and material damage, thus reducing costs (Ercanoglu, 2005) and provide information for urban development and land use planning. For assessing landslide hazard, different methodologies are available in the literature. These are mainly grouped as: qualitative and quantitative methods. The quantitative methods eliminate the problem of subjectivity of the qualitative methods. On the other hand, qualitative methods are simpler. The product of qualitative methods (e.g. Carrara et al., 1995) is usually susceptibility maps that do not provide information about the probability of sliding (Carrara and Merenda, 1976; Anbalagan, 1992; Soeters and Van Westen, 1996; Wachal and Hudak, 2000; Van Westen et al., 2003). Hazard mapping should contain information about probability of occurrence of a landslide in a given area over a specified period of time (Varnes, 1984). Effective landslide hazard maps can be constructed based on a combination of spatial and temporal predictions of landslide occurrence probability. Because of the lack of information on the dates of occurrence of past landslides, it is difficult to do temporal predictions of landslide occurrence. Hazard maps can be obtained through the use of available quantitative information, and provide an estimate of where landslides can be expected without having the information on when they have occurred (Ohlmacher and Davis, 2003). 2. APPROACH The present landslide-hazard map will show the probability of future landslides, for a set of influencing factors. To evaluate landslide hazard, a variety of statistical techniques are used (Anagnosti and Lesevic, 1991; Carrara, 1988; Carrara et al.,

2 1995; Van Westen, 1993; 1997; Chung and Leclerc, 1994; Van Westen et al., 1997; Beguería and Lorente, 1999; Chung and Fabbri, 1999; Lee and Min, 2001; Pistocchi, 2002; Ohlmacher and Davis, 2003; Ayalew and Yamagishi, 2005; Guzetti et al., 2005; Zhu and Huang, 2006). Hazard assessment of landslides involves the collection, manipulation and analysis of large amount of data (Fall et al., 2006). Geographical Information System (GIS) technology can provide a powerful tool to model and predict landslide hazard spatially. The handling and interpretation of environmental data and their effect on landslide hazard can be accomplished much more efficiently and cost-effectively with the use of GIS (Lan et al., 2004). In the studies of Carrara (1983), Wang and Unwin (1992), Van Westen (1993), Carrara et al. (1995), Sakellariou and Ferentinou (2001), Ohlmacher and John (2003), Lee (2005), Wang et al. (2005), and Zhu and Huang (2006), GIS technology is used for hazard zonation. In the present study, GIS was used for spatial management and data manipulation. For the assessment of hazard, the data relevant for landslide occurrence should be properly assessed. Influence factors which can trigger instability of a slope can be intrinsic or extrinsic variables. Intrinsic variables can be such as geological conditions, morphology and slope inclination (Wang et al., 2005). The landslides can also be triggered by a variety of external stimulus, such as intense rainfall, earthquake shaking, and human activities. The human activities, such as deforestation, excavation, road cuts or erection of buildings, etc., (Dai et al., 2002), have been important triggers for quick clay slides in Norway, especially with increasing population and urbanization. The urbanization should therefore be incorporated into the analysis. Other factors considered include: distance to railway and road, land use, slope inclination, curvature, elevation, precipitation, cone resistance, soil type, geological formation, and distance to river and lakes. The study included a total of 11 factors for assessing the hazard zones in the study region. The procedure for the assessment of landslide hazard was based on the widely accepted principle the past and present are keys to the future (Varnes, 1984). An accurate determination and mapping of the present and historical location of landslides is very important. For the creation of historical clay slide locations, hardcopy maps showing the locations of earlier clay slides were used. For data analysis, the landslide inventory map was digitised and rastered in GIS. To apply the frequency ratio model, a spatial database of landslide related factors was designed and constructed. With this spatial database, the landslide hazard zonation was done first with a univariate probability analysis. In this univariate probability analysis, called the frequency ratio model, the spatial relationship between the landslide locations and each landslide-related factor was analysed (Lee and Tu Dan, 2005; Lee and Pradhan, 2006; 2007). Frequency ratio values for each category of the influencing factors were computed using GIS tools. After the analysis of the landslide related factors and the landslide locations, the relationships were used for each factor's rating in the overlay analysis. The ratings of the factors were summed up to give a landslide hazard index and hazard map. The reliability of the landslide hazard maps produced by the frequency ratio method was assessed by comparing the results with the locations of earlier landslides. The study region covering 44.8 km 2, shown in Figure 1, is located on the south-eastern part of Norway. The municipality is located near the mouth of a river that runs out into a fjord. Most of the clay slides occur along the river. The characteristics of the quick clay are associated with leaching of the salt of the marine clay. During deglaciation at the end of the ice age, moraine deposits and materials from glacier stream deposits were built up. As the glaciers withdrew, lower areas were covered by thick marine sediments consisting of mainly silt and clay. During the post-glacial continental uplift, the marine deposits and other soils below the marine limit became dry land. Rivers and creeks started to erode the soils, moving material from higher to lower elevations. If marine clay is subjected to a slow flow of fresh ground-water so that the salt originally confined in the pore water of the clay is removed by leaching, it will be changed to quick clay. The effect of the leaching is a reduction in plasticity of the clay and a dramatic increase in sensitivity. Quick clays are prone to sudden collapse when disturbed or loaded above yield, resulting in sometimes disastrous landslides. The soil profile consists typically of a 1.5 m to 3.5 m thick river sand layer at the top. Below the sand, marine deposits, mainly consisting of clayey silt, with some fine sand layers are found. Below this, sand and gravel (glacial river deposits and moraine) extend down to bedrock. The transition from marine deposits to sand/gravel deposits is lower than the river water level, which means that the clay deposits are exposed to erosion. Erosion may trigger slides in the clay. This in turn represents a threat to the inhabitants and the properties along the river. The influencing parameters were collected and transformed into a spatial database to determine hazardous zones. 3. Creation of Thematic Maps Eleven factors were considered in the calculation for probabilities. In the study, the hazard assessment considered the gridcells as the mapping unit. The data sets used for the analysis are shown in the Table 1, including data type and scale. Erener et al. (2007) present the results for all variables studies. Because of space limitations for this paper, the results for only a limited number of variables are presented.

3 Figure 1. Area of study For the calculation of topographic factors a TIN (Triangular Irregular Network) was constructed by using 3D Analyst tool of ArcGIS 9.0. The TIN of the region was constructed by triangulating a set of vertices (Figure 2). The vertices were connected with a series of edges to form a network of triangles. The resulting triangulation satisfied the Delaunay triangle criterion, ensuring that no vertex lies within the interior of any of the circumcircles of the triangles in the network. The TIN created from the contour map of the region had 5 m intervals. This layer provided a continuous elevation surface of the area. The generated TIN was converted to a raster through interpolation. The altitude in the area ranges from 0 to 215 m above sea level. Every cell in the output was assigned a height or a NoData value depending on whether or not the cell centres fall within the TIN's interpolation zone. A DTM was constructed (Figure 3). The DTM created with 20 m x 20 m resolution was used to extract morphological factors that could cause instability of the slope. From the DTM, slope inclination and curvature were calculated. The calculated factors were in the form of grid (of the ARC/INFO grid type) with 20 m resolution, and are shown in the Figures 4 and 5 respectively.

4 Table 1. Data used for analysis Main Data Set Map Produced GIS Data Type Scale/Accuracy Classification Land use Map Land use Poligon 1/250,000 Land Cover Distance to streams GRID 20x20 Infrastructure data Distance to road and railway GRID 20x20 Infrastructure data Topographical Map Slope GRID 20x20 Topographical Curvature GRID 20x20 Topographical DEM GRID 20x20 Topographical Precipitation Map Precipitation GRID 1X1 km 2 Rainfall Data Geology Map Lithology Poligon 1:250,000 Geology Soil map Soil type Poligon 1:250,000 Structural Geology Clay Map Clay zone Poligon 1:50,000 Geology CPTU Cone resistance (q c) GRID 20x20 Geology The slope map of the region show that the inclination varies between 0 to 55 degrees in the region. The inclination affects the overall rate of movement downwards. Curvature describes the physical characteristics of a drainage basin, in an effort to include erosion and runoff processes. A positive curvature indicates that the surface is upwardly convex at that cell. A negative curvature indicates that the surface is upwardly concave at that cell. A value of zero indicates that the surface is flat. Beside topographic effects, the urbanization and antropogenic actions were also considered. To analyse these effects on clay slide, the distance to road and railroad, and the use of the land were included as factors. The main roads and railroad were combined into one single layer and called transportation layer. For distance calculations, the spatial analyst tool of ArcGIS was used. The map of distance to road and railway is given in Figure 6. The roads in the city were very closely knit. A distance of 75 m was selected as the boundary for the present study of roads and railroads. In the analysis, it will be seen that the quick clay slides occur more frequently with shorter distances to roads and railways. The land use map of the study region considered four different categories (Figure 7). Figure 2. TIN of the region (meter) Figure 3. DTM of the region (meter) The soil map included 5 different types (Figure 8). The Organic Material; Fluvial Deposit and Marine Deposit contain clay underneath. A map of the quick clay zones was also included in the analysis (red zones in Figure 9). The historical landslide locations were mapped. The locations of historical quick clay slides were acquired from the available hardcopy map. The historical slide locations are shown with red colour in Figure 10. The black contours drown on the map show the hazard zones determined in an earlier project by expert opinion. From this map, only the historical clay

5 slides which are drawn with red were digitised. Before the digitisation, the scanned map which was without coordinates was geo-referenced to overlay exactly to the other layers created (Figure 11). As a result seven quick clay slides were mapped for the study region. Figure 4. Slope map of the region (degrees) Figure 5. Curvature map of the region Urban area Empty fields Transportation corridors Water surface Figure 6. Distance to road and railroad map (meter) Figure 7. Land use map of the region To assess the incidence of every predisposing factor to clay slide, the maps were georeferenced to Universal Transverse Mercator (UTM). All clay slide locations and the factors considered for this study were then rastered by dividing the area into cells of 20 m at the side, thus obtaining a square-grid matrix with a total of 1041 cells for the earlier clay slide locations and 112,141 cells for the entire study region.

6 Quick clay zone Figure 8. Soil map of the region Figure 9. Quick clay zones of the region Figure 10. Map of historical quick clay slides in region Figure 11. Historical clay slide map of the region 4. Hazard Mapping The Frequency Ratio (FR) model (Lee, 2007) was used in this study to analyse the correlation between the historical clay slide locations to the various influencing factors under consideration. The influencing factors were compared with those of clay slide locations by evaluating how many of the pixels forming the slides fell into the various categories of the factors is analysed (Donati, 2002). This method allows the weighting of the influencing factors on clay slides as objectively as possible, in the assessment of the potential hazard zones. The drawback of subjectivity in rating the factors is removed, but the subjectivity in categorisation of continuous data formats remains. In the FR model, for each influencing factor, the percentage of the area where landslides occur is divided by the total percentage of the influencing factor in the study area. The spatial relationships can then be analysed. The frequency ratio FR is defined as:

7 PLO FR = (1) PIF where PLO is the relative area where landslide occurred in per cent for the given category of influencing factor and PIF is the relative area of the influencing factor for the given category of influencing factor. In Tables 2 to 6, PLO is obtained as A/B and PIF is computed as C/D. FR values greater than 1 indicate higher correlation. FR values much less than 1 (below 0.85) show low correlation. In the tables highly correlated variables to landslide locations, i.e. FR>1, are highlighted in bold. The relationship between landslide locations and each of the influencing factors was analysed Slope inclination The relationship between landslide locations and slope inclination suggests that (Table 2) slope inclination between 5 and 15 has highest probability. Slope angles larger than 15 do not show a distinct relationship to landslide occurrences shown in Figure 12. This result may be considered as unexpected, but is probably due to the fact that the clay may not consist of quick clay or slide-prone material. Table 2. Frequency ratio for the steepness map (factor = SLOPE, slope inclination) Class Landslide Occurrence * Pixels in domain ** A, Number PLO (%) C, Number PIF(%) Ratio 0~ ~ ~ ~ ~ > *B=1041 (total number of landslides containing pixels) **D=112,141 (total number of pixels in the study area) Frequency ratio Slope >25 (degrees) Elevation Figure 12. Frequency ratios for slope inclination A study of the elevation map suggests high correlation to landslide occurrence up to elevation 15 m (Figure 13, Table 3). This may also reflect closeness to the river where ground elevation is low, and may indirectly depend on erosion. Table 3. Frequency ratios for the elevation map (factor=dem) Class Landslide Occurrence * Pixels in domain ** A, Number PLO (%) C, Number PIF(%) Ratio 0-5 meter > *B=1041 (total number of landslide containing pixels) **D=112,141 (total number of pixels in the study area)

8 Frequency ratio Elevation >30 (meter) Figure 13. Frequency ratios for elevation Distance to road and railway The urbanization can be an important trigger of quick clay slides. This is clearly seen from the correlation with distance to road and railway. As can be seen from Table 4, the frequency ratio is above 1 up to a distance of 60 m, and reduces drastically thereafter (Figure 14). The somewhat lower FR at distances less than 10 m may be due to the direction of safety barriers near the roads. Table 4. Frequency ratio for distance to road and railway maps (factor=distance to road and railway) Class Landslide Occurrence * Pixels in domain ** A, Number PLO (%) C, Number PIF(%) Ratio <= >60 (meter) *B=1041 (total number of landslides containing pixels) **D=112,141 (total number of pixels in the study area) Frequency ratio Distance to Road <10 m >60 (meter) Figure 14. Frequency ratios for distance to road and railway Quick clay occurrence The analysis and function of the occurrence of quick clay also gave a convincing trend. Due to the location of slides outside the quick clay zones, a slight FR number was registered in a non-quick clay zone (Table 5 and Figure 15). Table 5. Frequency ratio for quick clay location Class Landslide Occurrence * Pixels in domain ** A, Number PLO (%) C, Number PIF(%) Ratio *B=1041 (total number of landslides containing pixels) **D=112,141 (total number of pixels in the study area)

9 6.00 Clay 5.00 Frequency ratio Urbanization and land use Figure 15. Frequency ratio figure of clay The land use was classified into 4 classes. The four categories were: 1 = urban; 2 = transportation corridors; 3 = Empty fields; 4 = water. The landslides occurred mostly in the urban areas. This result indicates the influence of urbanisation to the clay side occurrences which is suggested by Table 6 and Figure 16. Table 6. Frequency ratio for land use and urbanization Class Landslide Occurrence * Pixels in domain ** A, Number PLO (%) C, Number PIF(%) Ratio *B=1041 (total number of landslides containing pixels) **D=112,141 (total number of pixels in the study area) Frequency ratio Land Use Figure 16. Frequency ratio for land use and urbanization Mapping The FR model provided values for each influencing factor considered. Each analysed layer in the 20 x 20 m 2 grids was assigned the ratio calculated for each factor and each class. With the frequency ratio of each layer, the grids were overlain and a hazard mapping index was calculated by summing the indexes of each cell in each layer, using the GIS software. The summation had a value of between 0 to and a standard deviation of After calculation of the total frequency ratios for each cell, the hazard index was classified into 3 classes: low, medium and high susceptibility (Figure 17). For the classification of the three hazard classes, the natural boundary calculation method was applied. In this method, each range has data values that are fairly close together; in other words, the average of each range is as close as possible to each of the values in that range. The zones described as "high susceptibility" occur mostly on the banks of the river, or close to railway and main road. The urbanization on the right bank of the river causes the main effect for medium degree hazard because the patterns of urban and road can be clearly seen from the medium hazard zone on the right side of river. The hazard map overlaid with clay slide locations (Figure 18) agrees well. All the historical clay slide locations are within the high hazard zones.

10 Figure 17. Hazard map from probabilistic frequency ratio approach Figure 18. Hazard map from probabilistic frequency method overlaid with locations of historical quick clay slides The reliability of the approach was evaluated by computation of the percentage and density of landslide locations within each hazard zone. The density of landslide within the hazard zones was calculated by dividing the number of landslides in each zone of the total area of each hazard zone. The percentage indicates the clay slide occurrence predicted by each method

11 within each hazard zone. Table 7 shows a strong correlation of the observed landslides in the high hazard zone, as evaluated by the frequency ratio approach (86%). Hazard zone Table 7. Reliability of frequency ratio approach Number of earlier landslides Frequency Ratio Method Total Area Density Slide occurrence (%) LOW MEDIUM HIGH Summary and conclusions Hazard zonation for quick clay slides was done for an example site in southern Norway by the frequency ratio method. A series of 11 parameters were implemented in the data layers implemented via GIS. After superposing all 11 factors, maps of low, medium and high hazard were established. These were compared with the location of historical quick clay slides in the area. The approach gave convincing results, with a more realistic picture of earlier landslides where the high hazard zones correlated well with the observed landslides that occurred earlier. 6. Acknowledgement The study was partly supported by the European Commission through the Integrated Project LESSLOSS Risk Mitigation for Earthquakes and Landslides, under Contact , Global Change and Ecosystems, in the 6 th Framework Program. The authors would like to thank the Norwegian Geological Survey (NGU) for providing the geological data, and O. Gregersen and E. Skurtveit at NGI for their assistance. The first author would like to thank the International Centre for Geohazards for financing her stay at NGI. 7. References Anagnosti P, Lesevic Z (1991) Probabilistic versus deterministic approach in hazard assessment of landslides along man made reservoirs. Landslides, 2: Anbalagan R (1992) Terrain evaluation and landslide hazard zonation for environmental regeneration and land use planning in mountainous terrain. Proc. VI Int Symp on Landslides, Christchurch, New Zealand, 2: Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for susceptibility mapping in the Kakuda- Yahiko Mountains, Central Japan. Geomorphology, 65(1-2): Beguería S, Lorente A (1999) Hazard mapping by multivariate statistics: comparison of methods and case study in the Spanish Pyrenees. Debris fall assessment in mountain catchments for local end-users. Instituto Pirenaico de Ecología, CSIC, 202, Zaragoza, Spain. Carrara, A (1983) A multivariate model for landslide hazard evaluation. Mathematical Geology 15: Carrara A (1988) Multivariate models for landslide hazard evaluation. A black box approach. Workshop on Natural Disasters in European Mediterranean Countries, Perugia, Italy, pp Carrara A, Cardinali M, Guzetti F, Reichenbach P, (1995) GIS technology in mapping landslide hazard. Geographical Information Systems in Assessing Natural Hazards, Chung CF, Leclerc Y (1994) A Quantitative Technique for Zoning Landslide hazard. International Association for Mathematica Geology Annual Conference, Mont Tremblant, Québec: Van Westen CJ (1993) Application of Geographic Information System to landslide hazard zonation. ITC-Publication No. 15, ITC, Enschede, 245.

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