Landslide Susceptibility Assessment Using Statistical Models: a Case Study in Badulla District, Sri Lanka
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Ceylon Journal of Science 46(4) 2017: 27-41 DOI: http://doi.org/10.4038/cjs.v46i4.7466 RESEARCH ARTICLE Landslide susceptibility assessment using statistical models: A case study in Badulla district, Sri Lanka G.J.M.S.R. Jayasinghe1*, P. Wijekoon1 and J. Gunatilake2 1Department of Statistics & Computer Science, Faculty of Science, University of Peradeniya, Sri Lanka 2Department of Geology, Faculty of Science, University of Peradeniya, Sri Lanka Received:19/07/2017; Accepted:18/09/2017 Abstract:. Landslides are the most recurrent and practiced to reduce the destruction to some prominent natural hazard in many areas of the world extent. which cause significant loss of life and damage to properties. By generating landslide susceptibility When identifying the factors which are maps, the hazard zones can be identified in order to responsible for landslides, slope aspect, slope produce an early warning system to reduce the angle and elevation of a location can be damage. In this study, the predictive abilities of two statistical models, Logistic regression (LR) model and considered as some key natural factors. Slope Geographically Weighted logistic regression (GWLR) aspect is the direction of a slope which identifies model, were compared. As a case study, a data set the steepest downslope direction at a location. collected for nine relevant causative factors over the Several researchers have shown the relationship period from 1986 to 2014 was taken from Badulla between aspect and landslide occurrence district, Sri Lanka, which is highly affected by (DeGraff and Romesburg, 1980, Marston et al., landslides. The performance of each model was tested 1998). Also, according to the knowledge of by using the Area under the curve (AUC) value of experts, the probability of occurrence of Receiver operating characteristic curve (ROC), and landslides is high when the slope angle is in the GWLR model was selected as the best fitted model. The probabilities obtained for each pixel in the between 15 and 45 and it is relatively low study area using the selected model were classified when it is more than 45 . Moreover, landslides into three classes (Low, Medium and High) based on may occur in flat areas as well due to excessive standard deviation method in GIS software. Finally, a pressure placed on the respective area. The landslide susceptibility map was generated related to elevation also affects the landslide occurrences, the above three classes, from which high risk areas since it determines the severity of climate can be identified to take necessary actions. condition in mountain regions. Keywords: Landslide, Susceptibility, Geographic Slope curvature is another triggering Information Systems (GIS), Geographically factor, which measures the rate of change of Weighted logistic regression, Receiver operating characteristic curve slope. Using profile and plan curvature, the slope curvature can be identified. The profile curvature INTRODUCTION is the rate of change of slope parallel to slope gradient. A negative value of profile curvature Landslide is defined as a “movement of mass of implies that the surface is upwardly convex, a soil or rock down a slope” (Courture, 2011), and positive profile conveys that the surface is it is a common natural hazard in many areas of upwardly concave, and a value of zero indicates the world. Annually landslides cause many that the surface is flat. Plan curvature is the rate fatalities and property losses each year. In of change of slope perpendicular to slope addition, the damages can also caused on the gradient which influences convergence and environment due to loss of wildlife and forest divergence of flow. In this case, a positive value cover. However, the damages to ecosystems due implies that the surface is upwardly convex to landslides cannot be easily estimated. whereas a negative value indicates the surface is Therefore, landslide susceptibility analysis and upwardly concave. Both profile and plan mapping are important to identify the landslide curvature are two key factors responsible for hazard zones, and risk management can be landslides since these factors affect the *Corresponding Author’s Email: [email protected] http://orcid.org/0000-0002-0782-0445 28 Ceylon Journal of Science 46(4) 2017: 27-41 acceleration and deceleration of flow which In recent years, there have been many influences erosion. studies of landslide hazard mapping using Geographic Information System (GIS), and most Another key factor of landslide occurrence of these studies have applied probabilistic is the landuse type due to human activity. The methods. Lee et al. (2005) have carried out data related to landuse can be extracted from research on landslide susceptibility mapping in satellite images, and interpreted visually, Baguio City, Philippines using the frequency supported by supervised classification. ratio method, logistic regression and GIS. The Theoretically, barren land and cultivations are study was aimed to determine a relationship more prone to landslides. Further, the forest between landslide occurrences and conditioning areas tend to decrease landslide occurrences due factors, rating the predictors, and then to generate to the natural protection provided by the thick the landslide susceptibility map using the vegetation cover. selected model and GIS.They identified landslide locations using aerial photographs and field Landslides may also occur on the slopes surveys. The factors that they have considered intersected by roads. The cutting toe of the steep were the slope, aspect, curvature, the distance slope, filling along the road and frequent from drainage, lithology, the distance from fault vibrations caused by vehicles are some human and land cover. The results were finally validated activities in mountain areas that raise the using known landslide locations, and it was landslide susceptibility. According to Lee et al. found that the logistic regression model had (2005), the potential of landslides also increases higher predictive value (80 %) than that of the with a decrease in distance to rivers. Therefore, frequency ratio method (78.4%). the distance to streams is another factor to be considered in the susceptible landslide analyses. Modeling of road related and non-road related landslide hazard for a large geographical Many qualitative and quantitative methods area was carried out by Gorsevski et al. (2006) are available to analyze data related to landslides. by using logistic regression and receiver The different approaches considered in the operating characteristic (ROC) analysis. The literature can be identified as a heuristic, variables selected for this study were elevation, physically-based modeling, probabilistic and slope, aspect, profile curvature, plan curvature, statistical. The heuristic approach is based on the tangent curve, flow path length, and upslope opinion of geomorphologic experts. Experts area. The landslide data set was subdivided to define the weighting value for each causal factor road related, and non-road related landslides and in this method. Determining exact weighting two separate logistic regression models were values may cause incorrectness of results since obtained and compared. It was noted that spatial the method involves subject-oriented process. In prediction and predictor variables for those physically based modeling approach, slope models were different. Using ROC method, the stability model is obtained by calculating safety performance of the predictive rule of two models factor, and it is used to determine the landslide was measured. hazard assessment. The probabilistic approach is conducted by considering remote sensing data, Dieu et al. (2012) have conducted research field observations, data collected from an on landslide susceptibility assessment in interview, and results based on historical Vietnam by using three methods; data mining analysis. An obstacle to this method is that the approaches, the support vector machines (SVM), results do not present estimation of temporal and Decision Tree (DT) and Naïve Bayes (NB) changes in landslide distribution. In the statistical models. They have used a landslide inventory approach, the combinations of causal factors are having 118 landslides records, and then statistically determined and then forecasts future partitioned it randomly into 70% as training landslide areas based on past landslides. Weights dataset and remaining data to validate results. of evidence method, Information value method, Landslide susceptibility index was calculated by Frequency ratio method, Logistic regression considering slope angle, slope aspect, relief analysis and discriminant analysis are some of amplitude, lithology, soil type, land use, distance the methods that have been used under this to roads, distance to rivers, distance to faults and approach. rainfall as landslide causal factors. Finally, based on validation results and landslide Jayasinghe et al. 29 susceptibility maps, it was shown that SVM is a In 1986, 51 lives were lost and powerful tool for landslide susceptibility approximately 10,000 families were displaced in assessment with high accuracy with compared to Sri Lanka as a result of landslides. In a most other two methods. recent incident occurred in 2014, 13 died, and 1,875 were affected in Meeriyabedda in Badulla The main objective of this study is to district. These natural disasters cause major assess and evaluate the landslide susceptibility threats to the country‟s economy through using two types of statistical