- Methodology
- Open Access
Effect of Landslide Factor Combinations on the Prediction Accuracy of Landslide Susceptibility Maps in the Blue Nile Gorge of Central Ethiopia
- Matebie Meten^{1}Email author,
- Netra PrakashBhandary^{1} and
- Ryuichi Yatabe^{1}
https://doi.org/10.1186/s40677-015-0016-7
© Meten et al.; licensee Springer. 2015
- Received: 26 November 2014
- Accepted: 10 February 2015
- Published: 26 March 2015
Abstract
Database construction for landslide factors (slope, aspect, profile curvature, plan curvature, lithology, land use, distance from lineament & distance from river) and landslide inventory map is an important step in landslide susceptibility modelling. Using the frequency ratio model, the weights for each factor classes were calculated and assigned in GIS so as to add these factors and produce landslide susceptibility index maps based on mathematical combination theory. However, before combining them, their independence among each other should be ascertained. For this, the correlation matrix of logistic regression was applied and this showed that most of the correlations between factors were either absent or very insignificant suggesting that all landslide factors are independent. From a set of eight landslide factors, a total of 247 landslide susceptibility map combinations can be generated. However, for simplification, only 28 landslide susceptibility maps were chosen. Then the best landslide susceptibility map was selected based on high prediction accuracy. But, when there is similarity in the prediction accuracies of different combinations, the landslide susceptibility index difference values can be used as another selection criterion. Hence, the susceptibility map from a combination of all landslide factors except distance from river was found to be the best one. Among the 28 representative combinations, landslide susceptibility maps with the same prediction accuracy of 87.7% have been found in spite of their dissimilarity in their difference values. The combination, with a limited number of landslide factors and the highest prediction accuracy of 87.7%, was found from a combination of slope, lithology, land use and distance from lineament. In order to validate the prediction model, landslides were overlaid over the landslide susceptibility map and the number of landslides that fall into each susceptibility class was calculated. From this analysis 0.39%, 1.84%, 9.1%, 32.04% and 56.63% of the landslides fall in the very low, low, medium, high and very high landslide susceptibility classes respectively. Since 88.67% of the landslides fall in the high and very high susceptibility classes, the landslide susceptibility map can be considered reliable to predict future landslides.
Keywords
- Landslide susceptibility
- GIS
- Frequency ratio
- Combination
- Prediction accuracy
- Ethiopia
Background
Landslide is the movement of a mass of rock, debris or earth (soil) down a slope and landslide susceptibility is a quantitative or qualitative assessment of landslide about its classification, volume (or area) and spatial distribution (IUGS 1997, Fell et al. 2008). Landslide susceptibility mapping methods are classified into heuristic (Ruff and Czurda 2008), statistical (Lee et al. 2004; Pradhan et al. 2011), deterministic (Godt et al. 2008) and a combination of statistical and deterministic (Terlien 1998) methods. Susceptibility, hazard and risk maps are important tools for engineers, earth scientists, planners and decision makers select appropriate sites for agriculture, construction and other development activities (Ercanoglu and Gokceoglu 2002). They also play an important role in efforts to mitigate or prevent the disaster in landslide prone areas by providing important information to the concerned bodies. In heuristic methods, field observation and expert’s knowledge are used to identify landslides, make a prior assumption about past and future landslide movements on the site, assign weighted values for the classes of index maps and overlay them to produce a landslide susceptibility map. In deterministic method, data on slope geometry, shear strength (cohesion and angle of internal friction) and pore pressure are required (Regmi et al. 2010a). A significant limitation of deterministic models is the need for geotechnical data (cohesion, internal angle of friction, depth to groundwater table, degree of saturation etc.) which are difficult to obtain over large areas (Terlien et al. 1995).
Data from Ayalew (1999), Temesgen et al. (2001), Woldearegay (2008) and Ibrahim (Ibrahim J, 2011: Landslide assessment and hazard zonation in Mersa and Wurgessa, North Wollo, Ethiopia, unpublished Master Thesis) showed that landslide in Ethiopia has resulted loss of human lives, properties and infrastructures particularly in the last five decades. From 1960 to 2010 alone, about 388 people were dead, 24 people were injured and a great deal of agricultural lands, houses and infrastructures were affected. Landslide problem in the Abay (Blue Nile) Gorge is a serious challenge to the community residing in this area and to the road infrastructure that connects Addis Ababa to Bahir Dar. In 1960, a terrible landslide at Gembechi village within Bechet valley was responsible for the loss of 45 people (Ayalew 1999). On September 2, 1993 a landslide incidence occurred in the Blue Nile Gorge, which killed an ox, damaged agricultural fields, destroyed crops and as a result 700 households were stricken by food insecurity. Besides this the main road, which was 5 km south of Dejen town has been damaged with a displacement of 1.5 meters by the sliding mass (Tadesse T, Dessie T and Deresa K, 1994: Landslide incidence in the Blue Nile Gorge of East Gojam, Ethiopia. Geological Survey of Ethiopia, 823 report, 830-301-01, unpublished). The road damage is a common phenomenon of the mid to end of each rainy season (i.e. June 1 to September 30) due to the gradual weakening of the soft and weathered rocks by heavy rain and groundwater percolation through big columnar joints of basalt to the underlying limestone formation bearing mudstone and shale at its top and middle strata. For example, Asfaw (2010) reported a road damage near Goha Tsiyon town on September 5, 2009. Such incidences happened due to the progressive softening of weathered basalt and pyroclastic rocks by heavy rainfall, groundwater recharge through the columnar joints of basalt and by a gushing stream that crosses a road. The Goha Tsion-Dejen transect is an important transport corridor connecting Addis Ababa with the regions in the northwestern part of the country. However, it is affected by a complex landslide problem almost on a yearly basis. To overcome this problem, few researchers in the field of Geotechnics, Geoscience and slope stability have been undertaking investigations in the Blue Nile Gorge. Recently, GIS is becoming a powerful tool to study landslide susceptibility and hazard worldwide because of its analysis potential and capability. A continuous and up to date landslide susceptibility map is vital to planners, engineers and decision makers in order to devise appropriate landslide prevention and mitigation measures. In this regard, a statistical (probabilistic) model known as frequency ratio has been applied in the current study area. This model was chosen because it is easy to understand and simple to implement. Data input, output and analysis processes are fast and a huge amount of data can be handled and run quickly (Lee and Pradhan 2007; Lee et al. 2007).
Frequency ratio model avoids the lengthy procedures of raster to point data conversion in GIS, weight calculation in statistical software and switching from statistical software to GIS for the preparation of landslide susceptibility map unlike logistic regression and artificail neural network models. Besides this, it utilizes all the available data contrary to the other two models, which may use a limited proportion of the data because of the low data processing capacity of the statistical software. Using a frequency ratio model, Lee and Talib (2005) have found the prediction accuracy of 72.1% in Penang, Malaysia and Lee and Sambath (2006) have found a prediction accuracy of 86.97% in the Damrei Romel area of Cambodia. Lee and Pradhan (2007) have shown that the frequency ratio resulted a better prediction accuracy than the logistic regression at Selangor area in Malaysia. Similarly, Pradhan (2010a) showed that the validation result of the frequency ratio model in the Cameron catchment of Malaysia is slightly better than logistic regression and fuzzy logic models with a prediction accuracy of 89.25%.
Until now researchers, who were engaged in the this model, were simply summing all the frequency ratio raster maps of landslide factors (Lee and Sambath 2006) or exclude one factor and sum all the remaining ones (Lee and Talib 2005). However, the previous works lack ways of systematic combination, identifying the number of possible combinations, providing more than one selection criterion to select the best landslide susceptibility map and finding a combination with high prediction accuracy from a limited number of factors.
The current study tries to prepare different landslide susceptibility maps from eight landslide factors and landslide inventory with different combinations using frequency ratio model and make a comparison on the prediction accuracies of these combinations in order to select the best landslide susceptibility map. This will help to suggest a limited number of landslide factors that can produce a susceptibility map with the highest prediction accuracy similar to a combination using all or most of the landslide factors. The main objectives of this study are: (1) to apply the frequency ratio model using combination theory, (2) to identify the possible numbers of combinations, (3) to evaluate the effect of different combinations on the prediction accuracy of landslide susceptibility maps and (4) to select the best landslide susceptibility map among different alternatives. In light of this, the following questions will be addressed in the subsequent chapters of this paper. (1) How many combinations are possible in the frequency ratio model with a certain number of landslide factors? (2) Which combination of landslide factors will give the best prediction accuracy? (3) How can we prioritize if the two landslide susceptibility maps have the same prediction accuracy? (4) How can we identify the best landslide susceptibility map obtained from a limited number of landslide factors?
Study area
Methods
Landslide inventory
A landslide inventory map, consisting 595 landslides, was prepared from field observations and Google earth images of the study area (Figure 2). Landslides in the area include rock slides, rock falls, debris slide and mudflow. According to Ayalew and Yamagishi (2004) rock falls exist as discernible block topples and wedge failures along the mountains, valley walls and road cuts. Similarly, rock slides are also abundant on the ridge sides and valley walls. The intensity of landslides is generally high in the upper catchments of Bechet and Muga valleys and on the road cut near GohaTsiyon town.
Landslide factors
The landslide factors used in this paper include lithology, distance from lineament, land use, distance from river, slope, aspect, plan and profile curvatures (Figure 4). The landscapes are greatly influenced by tectonics, bedrock lithology and the courses of major rivers. The complex processes of tectonics, erosion and sedimentation generates water gaps, knick points, meanders and many other tectonic and geomorphic features (Pirasteh et al. 2009). Tectonics may probably promote river incision in one side and river aggradation to the other side and rivers respond in different ways to similar tectonic scenarios. The main effects of tectonics are localized changes in the river course and changes in local topography (Pirasteh et al. 2009).
Lithology
Mesozoic Limestone is yellowish gray and light gray in color, mostly fossiliferous, medium to thickly bedded and forms gentle to steep cliffs. The limestone forms a bed thickness of 0.25 – 0.5 m and sometimes it may reach up to 1 m. The Tertiary Lower Basalt forms a steep morphology unconformably overlying the Mesozoic Limestone. It is dark gray, fine to medium grained, aphanitic basalt, plagioclase phyric and olivine - plagioclase phyric basalts. The basalt in GohaTsiyon – Dejen Road shows a spectacular columnar jointing and triggers a critical landslide problem. The Tertiary Upper Basalt is dark gray, fine to medium grained rock, consisting plagioclase phyric, olivine phyric and aphanitic basalts overlying thin beds of pyroclastic rocks. Lastly, in-situ weathering of the Tertiary basalts has given rise to the development of Quaternarysoil on the Dejen plateau.
Land Use
Frequency ratio value calculation by rationing landslide percentage to area percentage
Factor | Class | # landslide | % landslide | # area | % area | FRV = |
---|---|---|---|---|---|---|
Pixels | Pixels ^{ a } | Pixels | Pixels ^{ b } | (a/b) | ||
Slope (°) | 0 - 5 | 60 | 0.388 | 38089 | 8.770 | 0.04 |
5 - 10 | 333 | 2.151 | 87417 | 20.129 | 0.11 | |
10 - 15 | 811 | 5.239 | 102933 | 23.702 | 0.22 | |
15 - 20 | 1430 | 9.238 | 77279 | 17.794 | 0.52 | |
20 - 30 | 4195 | 27.099 | 80829 | 18.612 | 1.46 | |
30 - 40 | 4589 | 29.645 | 34095 | 7.851 | 3.78 | |
40 - 67 | 4062 | 26.240 | 13645 | 3.142 | 8.35 | |
Aspect | Flat | 5 | 0.03 | 1234 | 0.284 | 0.11 |
N | 424 | 2.74 | 29729 | 6.845 | 0.40 | |
NE | 1013 | 6.54 | 44175 | 10.172 | 0.64 | |
E | 2127 | 13.74 | 64829 | 14.928 | 0.92 | |
SE | 3136 | 20.26 | 60584 | 13.950 | 1.45 | |
S | 2306 | 14.90 | 52443 | 12.076 | 1.23 | |
SW | 3144 | 20.31 | 73195 | 16.854 | 1.21 | |
W | 2366 | 15.28 | 67137 | 15.459 | 0.99 | |
NW | 959 | 6.20 | 40961 | 9.432 | 0.66 | |
Plan curvature | - 10.7829 - - 1.7917 | 620 | 4.005 | 2810 | 0.647 | 6.19 |
- 1.7917 - - 0.9103 | 1704 | 11.008 | 20924 | 4.818 | 2.28 | |
- 0.9103 - - 0.1111 | 4986 | 32.209 | 154198 | 35.506 | 0.91 | |
- 0.1111 - 0.1111 | 1724 | 11.137 | 78020 | 17.965 | 0.62 | |
0.1111 - 0.5882 | 3117 | 20.136 | 121053 | 27.874 | 0.72 | |
0.5882 - 1.3815 | 2315 | 14.955 | 49643 | 11.431 | 1.31 | |
1.3815 - 11.7830 | 1014 | 6.550 | 7639 | 1.759 | 3.72 | |
Profile curvature | - 10.9947 - - 2.3629 | 767 | 4.955 | 3648 | 0.840 | 5.90 |
- 2.3629 - - 1.0810 | 1984 | 12.817 | 23076 | 5.314 | 2.41 | |
- 1.0810 - - 0.1154 | 4045 | 26.130 | 151550 | 34.896 | 0.75 | |
- 0.1154 - 0.1154 | 1332 | 8.605 | 74725 | 17.206 | 0.50 | |
- 0.1154 - 0.7991 | 3358 | 21.693 | 134940 | 31.072 | 0.70 | |
0.7991 - 1.9956 | 2737 | 17.681 | 40629 | 9.355 | 1.89 | |
1.9956 - 10.8837 | 1257 | 8.120 | 5719 | 1.317 | 6.17 | |
Distance from river | 0 - 100 | 5975 | 38.60 | 173853 | 40.032 | 0.96 |
(m) | 100 - 200 | 3728 | 24.08 | 116838 | 26.903 | 0.90 |
200 - 300 | 2305 | 14.89 | 63633 | 14.652 | 1.02 | |
300 - 400 | 1323 | 8.55 | 34376 | 7.916 | 1.08 | |
400 - 500 | 810 | 5.23 | 19283 | 4.440 | 1.18 | |
500 - 600 | 480 | 3.10 | 11048 | 2.544 | 1.22 | |
600 - 700 | 350 | 2.26 | 6352 | 1.463 | 1.55 | |
700 - 800 | 223 | 1.44 | 3866 | 0.890 | 1.62 | |
800 - 900 | 102 | 0.66 | 2253 | 0.519 | 1.27 | |
900 - 1000 | 49 | 0.32 | 1351 | 0.311 | 1.02 | |
1000 - 1100 | 74 | 0.48 | 748 | 0.172 | 2.78 | |
1100 - 1200 | 47 | 0.30 | 485 | 0.112 | 2.72 | |
1200 - 1300 | 14 | 0.09 | 150 | 0.035 | 2.62 | |
1300 - 1400 | 0 | 0.00 | 46 | 0.011 | 0.00 | |
1400 - 1500 | 0 | 0.00 | 5 | 0.001 | 0.00 | |
Factor | Class | # landslide | % landslide | # area | % area | FRV = |
Pixels | Pixels^{a} | Pixels | Pixels^{b} | (a/b) | ||
Distance from | 0 - 200 | 11297 | 72.978 | 108715 | 25.033 | 2.92 |
Lineament (m) | 200 - 400 | 2017 | 13.030 | 84627 | 19.486 | 0.67 |
400 - 600 | 930 | 6.008 | 61629 | 14.191 | 0.42 | |
600 - 800 | 487 | 3.146 | 47825 | 11.012 | 0.29 | |
800 - 1000 | 236 | 1.525 | 36076 | 8.307 | 0.18 | |
1000 - 1200 | 169 | 1.092 | 26915 | 6.198 | 0.18 | |
1200 - 1400 | 175 | 1.130 | 23374 | 5.382 | 0.21 | |
1400 - 1600 | 119 | 0.769 | 18887 | 4.349 | 0.18 | |
1600 - 1800 | 42 | 0.271 | 12387 | 2.852 | 0.10 | |
1800 - 2000 | 6 | 0.039 | 7334 | 1.689 | 0.02 | |
2000 - 2200 | 2 | 0.013 | 3926 | 0.904 | 0.01 | |
2200 - 2400 | 0 | 0.000 | 1634 | 0.376 | 0.00 | |
2400 - 2600 | 0 | 0.000 | 576 | 0.133 | 0.00 | |
2600 - 2800 | 0 | 0.000 | 281 | 0.065 | 0.00 | |
2800 - 3000 | 0 | 0.000 | 101 | 0.023 | 0.00 | |
Land use | Agricultural land | 7518 | 48.566 | 290914 | 66.987 | 0.725 |
Sparse forest | 706 | 4.561 | 10747 | 2.475 | 1.843 | |
Rural settlement | 55 | 0.355 | 16384 | 3.773 | 0.094 | |
Barren land | 4085 | 26.389 | 12423 | 2.861 | 9.225 | |
Bushes | 2813 | 18.172 | 88635 | 20.409 | 0.890 | |
River | 0 | 0 | 10049 | 2.314 | 0 | |
Dense forest | 171 | 1.105 | 1347 | 0.310 | 3.562 | |
Grass land | 39 | 0.252 | 1357 | 0.312 | 0.806 | |
Shrubs | 93 | 0.601 | 999 | 0.230 | 2.612 | |
Urban settlement | 0 | 0 | 1432 | 0.330 | 0 | |
Lithology | Quaternary soil | 184 | 1.189 | 6221 | 1.432 | 0.83 |
Tertiary lower basalt | 7733 | 49.955 | 94821 | 21.834 | 2.29 | |
Mesozoic limestone | 4011 | 25.911 | 129156 | 29.740 | 0.87 | |
Mesozoic gypsum, Mudstone and shale | 1170 | 7.558 | 140797 | 32.420 | 0.23 | |
Mesozoic lower sandstone | 2086 | 13.475 | 46758 | 10.767 | 1.25 | |
Paleozoic sandstone | 55 | 0.355 | 12044 | 2.773 | 0.13 | |
Tertiary upper basalt | 241 | 1.557 | 4490 | 1.034 | 1.51 |
Distance from lineament
Lineaments, which are found along steep linear ridges in the Abay (Blue Nile) gorge, have a strong influence in conditioning landslide incidences provided that the other favorable factors are also set in place. As can be seen in Table 1, the frequency ratio values for the distance from lineament showed higher values in the distance range of 0 to 200 m. The other distance classes revealed a less number of landslides. The surface rupture intensity is also influenced by distance from lineament or fault and ground conditions. As the distance from the lineaments becomes smaller, the fracture of the rock masses and the degree of weathering increases resulting in greater chances of landslide occurrence (Farrokhnia et al. 2010).
Distance from river
Rivers usually play a significant role in modifying the landscape by incising the different rocks. In the study area, the Abay (Blue Nile), Bechet and Muga Rivers and many other streams incised the Tertiary Volcanic rocks and Paleozoic and Mesozoic sedimentary rocks to a maximum depth of 1.5 km. The role played by rivers in creating a conducive environment for landslide occurrence has great significance. The maximum number of landslides in the close proximity of rivers, as can be seen in Table 1, shows how rivers are contributing to landsliding. In the steep-walled river banks of Bechet and Muga, landslides are common, particularly in fractured Tertiary Lower Basalt and the underlying Mesozoic Limestone units.
Slope
Slope is one of the most important topographic parameters influencing the occurrence of landslides in the study area. The landslide frequency is higher in the slope classes of 20 - 30°, 30 - 40° and 40 - 67° and the highest one is recorded in the slope class of 30 - 40° (Table 1). Generally speaking, as slope increases, the probability of landslide occurrence also increases.
Aspect
Aspect (slope orientation) affects the exposure to sunlight, wind and precipitation thereby indirectly affecting other factors that contribute to landslides such as soil moisture, vegetation cover and soil thickness (Clerici et al. 2006). The aspect of the area is classified into flat, north, northeast, east, southeast, south, southwest, west and northwest facing classes (Figure 4b). The number of landslides is higher in the aspect classes of E, SE, S, SW and W but the frequency ratio values of SE, S and SW facing slopes were found to be significant in causing landslides.
Profile and plan curvatures
Profile and plan curvatures are used for hill-slope and landslide analysis (Ayalew and Yamagishi 2004). (Ohlamacher 2007) presented a detailed account of plan curvature and its effect on hill-slope stability in earth flow and earth slides dominated regions. Plan curvature is the curvature of the topographic contours or the curvature of a line formed by the intersection of an imaginary horizontal plane with the ground surface. Hillsides can be concave outward plan curvatures called hollows, convex outward plan curvatures called noses and straight contours called planar regions. In hollows landslide material converges into the narrow region at the base of the slope. Profile curvature is the curvature in the downslope direction along a line formed by the intersection of an imaginary vertical plane with the ground surface (Ohlamacher 2007). Both profile and plan curvatures affect the susceptibility to landslides. Profile curvature affects the driving and resisting stresses within a landslide in the direction of motion. Plan curvature controls the convergence or divergence of landslide material and water in the direction of landslide motion (Carson and Kirkby 1972). The sign of the curvature value is important for determining concavity or convexity of the curve. In both profile and plan curvature maps, concave and convex surfaces are represented by the respective negative and positive values (Pradhan 2010a). Based on the plan curvature hill-slopes can be subdivided into hollows, noses and relatively planar regions. Hollows are regions in which the plan curvature of the contours is concave in the downslope direction and where surface water would converge as it moves downslope (Reneau and Dietrich 1987). Noses or coves are regions where the plan curvature of the contours is convex in the downslope direction and the surface water will diverge (Hack and Goodlett 1960). Relatively planar regions have plan curvature values around zero. Hollows concentrate groundwater and the concentration of groundwater probably leads to increased landslide activity.
Triggering factor
Theory
Frequency ratio method
The assumption of conditions that are similar to the past is very important for landslide susceptibility mapping (Lee and Talib 2005). Probabilistic (statistical) approaches are based on relationships between each landslide factor and the distribution of past landslides (Lee and Talib 2005) and this relationship can be evaluated quantitatively using the frequency ratio model. The eight landslide factors that are used in this study include lithology, land use, distance from lineament, distance from river, slope, aspect, profile and plan curvatures were used to establish this relationship with landslides (Table 1).
The number of landslide pixels in each class has been evaluated and the frequency ratio for each factor class is found by dividing the landslide ratio by the area ratio (Lee and Talib 2005). Frequency ratio shows the correlation between landslides and causative factors in a specific area. If this ratio is greater than 1, then the relationship between a landslide and the factor’s class will be strong but if the ratio is less than 1, then the relationship will be weak and if the value is 1, it means an average correlation (Lee and Sambath 2006; Pradhan 2010a). Once the frequency ratio of each landslide factor's class was found, the landslide susceptibility index (LSI) can be calculated by summation of each factor’s frequency ratio values (Lee and Sambath 2006). A higher LSI means a higher susceptibility to landslide while a lower LSI indicates a lower susceptibility to landslides (Bui et al. 2012).
Where Fr is the raster map of each landslide factor in which the frequency ratio values (FRV) are assigned to it. The current study tries to analyze the effect of different combinations of landslide factors on the performance of the frequency ratio model in order to get the minimum number of landslide factors which can produce a susceptibility map with higher prediction accuracy similar to combining many landslide factors using the mathematical combination theory.
Mathematical combination theory
Where k ≤ n, and which is zero when k > n.
Combinations refer to the combination of n things taken k at a time without repetition.
Representative combinations of factors from each group giving the best landslide susceptibility maps using frequency ratio method
No. | Selected frequency ration maps | Prediction accuracy (%) | # of landslide factors used | # of possible combinations | Min LSI (b) | Max LSI (a) | Difference (a-b) |
---|---|---|---|---|---|---|---|
1 | sl+as+pr+pl+li+lu+dl+dr | 87.6 | 8 | 1 | 2.54 | 38.95 | 36.41 |
2 | sl+as+pr+pl+li+dl+dr | 86.2 | 7 | 8 | 2.46 | 29.73 | 27.27 |
3 | sl+as+pr+pl+li+lu+dl | 87.7* | 1.58 | 36.59 | 35.01 | ||
4 | sl+pr+pl+li+lu+dl+dr | 87.6 | 2.37 | 37.74 | 35.37 | ||
5 | sl+as+pr+pl+li+lu+dr | 2.36 | 36.03 | 33.67 | |||
6 | sl+as+pr+pl+lu+dl+dr | 2.41 | 36.66 | 34.25 | |||
7 | sl+as+pl+li+lu+dl+dr | 87.7* | 2.04 | 32.80 | 30.76 | ||
8 | sl+as+pr+li+lu+dl+dr | 87.7* | 1.92 | 32.76 | 30.84 | ||
9 | sl+as+pr+pl+li+lu | 86.9 | 6 | 28 | 1.4 | 33.67 | 32.27 |
10 | sl+pr+pl+li+dl+dr | 86.1 | 2.29 | 28.70 | 26.41 | ||
11 | sl+as+pr+pl+li+dr | 84.9 | 2.36 | 26.81 | 24.45 | ||
12 | sl+as+pr+li+dl+dr | 86.3 | 1.79 | 23.90 | 22.11 | ||
13 | sl+as+li+lu+dl+dr | 87.6 | 14.42 | 26.61 | 25.19 | ||
14 | sl+pr+pl+li+lu | 86.8 | 5 | 56 | 1.29 | 32.20 | 30.91 |
15 | sl+as+li+lu+dr | 87.0 | 1.24 | 23.69 | 22.45 | ||
16 | sl+as+li+dl+dr | 86.2 | 1.28 | 17.79 | 16.51 | ||
17 | li+sl+dl+dr | 86.1 | 4 | 70 | 1.17 | 16.34 | 15.17 |
18 | li+sl+lu+dr | 86.8 | 1.07 | 22.46 | 21.39 | ||
19 | li+sl+pr+lu | 87.0 | 0.67 | 26.03 | 25.36 | ||
20 | sl+as+pl+lu | 86.4 | 0.77 | 25.21 | 24.44 | ||
21 | sl+li+lu+dl | 87.7* | 0.27 | 22.78 | 22.51 | ||
22 | sl+pl+li+lu | 86.9 | 0.79 | 26.05 | 25.56 | ||
23 | sl+as+li | 85.3 | 3 | 56 | 0.28 | 12.09 | 11.81 |
24 | sl+dl+li | 86.3 | 0.27 | 13.57 | 13.30 | ||
25 | sl+as+dr | 84.7 | 1.07 | 13.42 | 12.35 | ||
26 | Sl+li+lu | 86.9 | 0.17 | 19.86 | 19.69 | ||
27 | li+sl | 85.0 | 2 | 28 | 0.17 | 10.64 | 10.47 |
28 | sl+dr | 84.3 | 0.04 | 11.13 | 11.09 |
Result and discussion
In order to apply the frequency ratio model, the most important first step is to prepare a database of landslide factors and a landslide inventory map. This involves digitizing polygon features like lithology, land use and landslide inventory; line feature like lineaments and rivers and preparing digital elevation model (DEM) derivatives such as slope, aspect, profile and profile and plan curvatures in Arc GIS 10. The distance from lineament and distance from rivers are obtained from multiple ring buffering operation. Then all the data should be transformed into a raster format with the same geographic projection and pixel size (30 m). The frequency ratio model was applied to obtain a weight for each class in a certain factor. From the frequency ratio analysis, slope classes ≥ 20° have shown a strong correlation with landslides. In case of aspect, the southeast, south and southwest facing slopes showed a strong correlation with landslides. For profile and plan curvatures, the higher positive values and the lower negative values showed a strong relationship with landslides. Distance from lineament and landslides showed a strong relationship. As the distance from lineament decreases, the frequency ratio values become higher. In a the distance class of 0–200 m, the highest landslide frequency is recorded. From land use classes, barren land, sparse forest and grassland classes showed a strong relation with landslides. Among lithologic units in the area, Mesozoic Lower Sandstone, Tertiary Lower Basalt and Tertiary Upper Basalt showed a strong relationship with landslides. The frequency ratio values were assigned to each factor classes and all these raster maps of landslide factors were added to produce the landslide susceptibility index maps based on the mathematical combination theory.
Correlation matrix of landslide factors
Distance from lineament | Land use | Slope | Lithology | Aspect | Plan curvature | Profile curvature | Distance from river | |
---|---|---|---|---|---|---|---|---|
Distance from lineament | 1 | −0.085 | −0.249 | −0.39 | −0.076 | −0.005 | −0.037 | −0.062 |
Land use | 1 | −0.086 | −0.07 | −0.058 | 0.004 | −0.008 | 0.055 | |
Slope | 1 | −0.002 | −0.01 | −0.165 | −0.294 | 0.011 | ||
Lithology | 1 | −0.077 | −0.002 | −0.043 | −0.138 | |||
Aspect | 1 | 0.015 | −0.004 | 0.007 | ||||
Plan curvature | 1 | −0.336 | 0.005 | |||||
Profile curvature | 1 | −0.004 | ||||||
Distance from river | 1 |
For validation purpose of landslide susceptibility maps, many researchers divided the landslides in their respective study area into two parts based on time, space and random partitions (Chung and Fabbri 2003). These partitions fall into two categories: prediction (training) landslides and validation (testing) landslides. In time partition, past landslides are classified into landslides that occurred before a certain year X and those that occurred after a certain year X. In space partition, the entire study area is divided into two separate sub areas, A and B, one for prediction and the other for validation. By using the space-partition technique, the prediction model in the study area can be extended into the surrounding areas with similar geology, geomorphology and land use conditions. To know how much the prediction can be extended in space Chi et al. (2002) divided the entire study area into a northern and southern sub-areas because of the area's similarity in many aspects. Lee et al. (2007) divided into western and eastern areas for training and validation purposes respectively. In random partition, the past landslides are randomly divided into two groups instead of two time periods.
Conclusion
From this study, we have found that the mathematical combination theory is an important technique to identify the possible number of combinations in the frequency ratio model. This paper showed that using all landslide factors in the frequency ratio model may not always result in higher prediction accuracy even though the range of values in the susceptibility index map is higher. For example, the combination of 8 landslide factors results a prediction accuracy of 87.6%, while the combinations of all landslide factors except distance from the river provided an accuracy 87.7%. This shows that distance from river is less important as compared to other factors. But the landslide susceptibility index (LSI) difference value always higher in the combination with higher number of landslide factors. On the other hand, different combinations may result the same and high prediction accuracy. For instance, the combination from seven landslide factors (except distance from river) and the combination from four landslide factors (slope, lithology, land use and distance from lineament) showed the same prediction accuracy of 87.7%. This showed that these four landslide factors should have a greater degree of influence in causing landslides. A prediction accuracy as high as 85% was also possible from a combination of slope & lithology only. This demonstrates how these two factors are very much important in causing landslide occurrence. High prediction accuracy & LSI difference values are used to select the best landslide susceptibility map. By selecting 2 to 8 numbers of landslide factors from a set of 8 landslide factors, a total of 247 landslide susceptibility map combinations are possible. However 28 combinations were selected based on higher prediction accuracy from success rate curves, higher LSI difference values and through visual inspection of output susceptibility maps. An optimum landslide susceptibility map was prepared from four landslide factors (lithology, slope, land use and distance from lineament) while the best landslide susceptibility map was obtained from the combination of 7 landslide factors excluding distance from river (Figure 7a, b). Both maps have the prediction accuracy of 87.7% but with different LSI difference values of 35.01 and 25.51 respectively. In order to highlight the prediction accuracy contrasts of landslide susceptibility maps from success-rate curves were chosen from the 28 combinations as shown in Table 2.
Once the most important landslide factors are determined in a certain area, then these can be used to scale up the investigation at the regional level using these causative landslide factors. When landslide inventory map is overlaid over the best landslide susceptibility map, most of the landslides fall in the high and very high susceptibility classes accounting for 88.67% of the landslides. Besides this the success-rate of this map is being 87.7%, proving that the landslide susceptibility map from the frequency ratio model in the study area is quite acceptable. After the best and optimal landslide susceptibility maps (Figure 7 a, b) are selected these maps are divided into five categories and are expressed as probabilities in qualitative terms of very low, low, medium, high and very high susceptibility classes. Using this output, proper planning can be made to prevent, reduce or mitigate the possibility of future landslide disasters in this area. Creating awareness about the risk of high and very high susceptible zones to the general public will help to save the lives and properties of the people. Susceptibility, hazard and risk maps are the basis for decision making usually in the form of technical countermeasures, regulatory measures or combinations of the two (Pradhan et al. 2011).
Declarations
Acknowledgements
The first author would like to thank Japan’s Ministry of Education, Culture, Science and Technology (MEXT) for the scholarship grant to pursue the PhD study.
Authors’ Affiliations
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