GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey)

Yılmaz, Çağatay; Topal, Tamer; Süzen, Mehmet

doi:10.1007/s12665-011-1196-4

Cited by 130 publications

(57 citation statements)

References 121 publications

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“…The plan curvature of the mountain is the rate of change of the slope angle, which directly affects surface runoff and the development of landslides [47,48]. Plan curvature includes three types: concave, convex, and flat.…”

Section: Influencing Factorsmentioning

confidence: 99%

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China

Wang

et al. 2017

IJGI

106

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Abstract:The Longzi River Basin in Tibet is located along the edge of the Himalaya Mountains and is characterized by complex geological conditions and numerous landslides. To evaluate the susceptibility of landslide disasters in this area, eight basic factors were analyzed comprehensively in order to obtain a final susceptibility map. The eight factors are the slope angle, slope aspect, plan curvature, distance-to-fault, distance-to-river, topographic relief, annual precipitation, and lithology. Except for the rainfall factor, which was extracted from the grid cell, all the factors were extracted and classified by the slope unit, which is the basic unit in geological disaster development. The eight factors were superimposed using the information content method (ICM), and the weight of each factor was acquired through an analytic hierarchy process (AHP). The sensitivities of the landslides were divided into four categories: low, moderate, high, and very high, respectively, accounting for 22.76%, 38.64%, 27.51%, and 11.09% of the study area. The accuracies of the area under AUC using slope units and grid cells are 82.6% and 84.2%, respectively, and it means that the two methods are accurate in predicting landslide occurrence. The results show that the high and very high susceptibility areas are distributed throughout the vicinity of the river, with a large component in the north as well as a small portion in the middle and the south. Therefore, it is necessary to conduct landslide warnings in these areas, where the rivers are vast and the population is dense. The susceptibility map can reflect the comprehensive risk of each slope unit, which provides an important reference for later detailed investigations, including research and warning studies.

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Section: Influencing Factorsmentioning

confidence: 99%

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China

Wang

et al. 2017

IJGI

106

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“…When the strike of rock beds is perpendicular to the azimuth of mountain faces, the outcome are orthoclinal-slopes. As for slope curvature, it acts as major contributor to terrain instability in the way that it influence the concentration of the soil/rock moisture [44] and therefore influencing rockfalls. Though elevation by itself is not a conditional factor, there are some altitude ranges where the slope failures are frequent [32].…”

Section: Conditional Factorsmentioning

confidence: 99%

Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity

Deng

Tan

2017

IJGI

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Abstract:In this paper, we propose a methodology for validating landslide susceptibility results in the Pinggu district (Beijing, China). A landslide inventory including 169 landslides was prepared, and eight factors correlated to landslides (lithology, tectonic faults, topographic elevation, slope gradient, aspect, slope curvature, land use, and road network) were processed, integrating two techniques, namely the frequency ratio (FR) and the certainty factor (CF), in a geographic information system (GIS) environment. The area under the curve (success rate curve and prediction curve) analysis was used to evaluate model compatibility and predictability. Validation results indicated that the values of the area under the curve for the FR model and the CF model were 0.769 and 0.768, respectively. Considering spatial correlation, an alternative complementary method for validating landslide susceptibility maps was introduced. The spatially approximate maps could be discriminated from their matrices which carry structural information, and the structural similarity index (SSI) was then proposed to quantify the similarity. As a specific example, the SSI value of the FR (74.15%) scored higher than that of the CF model (69.36%), demonstrating its promise in validating different landslide susceptibility maps. These results show that the FR model outperforms the CF model in producing a landslide susceptibility map in the study area.

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“…These samples are chosen based on the completeness of the inventory, size of the study area, and the type of approach used. While there are currently no standard protocols for the selection of training and testing samples, it is important that the training and testing samples be independent of each other Yilmaz et al 2012;Yilmaz et al 2013). Basheer and Hajmeer (2000) have documented different splitting ratios used for various statistical methods.…”

Section: Training and Test Samplesmentioning

confidence: 99%

Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study)

Mahalingam

Olsen

O'Banion

2016

Geomatics, Natural Hazards and Risk

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Landslides are a significant geohazard, which frequently result in significant human, infrastructure, and economic losses. Landslide susceptibility mapping using GIS and remote sensing can help communities prepare for these damaging events. Current mapping efforts utilize a wide variety of techniques and consider multiple factors. Unfortunately, each study is relatively independent of others in the applied technique and factors considered, resulting in inconsistencies. Further, input data quality often varies in terms of source, data collection, and generation, leading to uncertainty. This paper investigates if lidar-derived data-sets (slope, slope roughness, terrain roughness, stream power index, and compound topographic index) can be used for predictive mapping without other landslide conditioning factors. This paper also assesses the differences in landslide susceptibility mapping using several, widely used statistical techniques. Landslide susceptibility maps were produced from the aforementioned lidar-derived data-sets for a small study area in Oregon using six representative statistical techniques. Most notably, results show that only a few factors were necessary to produce satisfactory maps with high predictive capability (area under the curve >0.7). The sole use of lidar digital elevation models and their derivatives can be used for landslide mapping using most statistical techniques without requiring additional detailed data-sets that are often difficult to obtain or of lower quality.

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GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey)

Cited by 130 publications

References 121 publications

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China

Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity

Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study)

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