Structural and geomorphological aspects of the Kat landslides (Tokat—Turkey) and susceptibility mapping by means of GIS

Yilmaz, Işık; Yıldırım, Mustafa

doi:10.1007/s00254-005-0107-y

Cited by 40 publications

(10 citation statements)

References 4 publications

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“…A number of different models have been developed in order to assess landslide susceptibility, and these maps were produced by use of deterministic and non-deterministic (probabilistic) models. The probabilistic models are more frequently used, and a large number of methodologies have been developed (Rengers et al 1998), based on the inventory of landslides, geomorphological analysis, qualitative and statistical bivariate analysis (Brabb et al 1972;DeGraff and Romesburg 1980;Jade and Sarkar 1993;Irigaray 1995;Chung and Fabbri 1999;Fernández et al 2003;Yilmaz and Yildirim 2006;Yilmaz 2008), and multivariate analysis (Carrara et al 1991;Baeza 1994;Chung et al 1995). Many researchers have used different techniques such as heuristic approach (Ives and Messerli 1981;Rupke et al 1988; Barredo et al 2000;Van Westen et al 2000;Van Westen and Lulie 2003), deterministic models (Ward et al 1982;Cascini et al 1991;Gokceoglu and Aksoy 1996), and statistical methods (Van Westen 1993;Chacón et al 1994Chacón et al , 1996Chung and Fabbri 1999;Dai et al 2001;Lee and Min 2001;Carrara et al 2003;Yilmaz 2008).…”

Section: Introductionmentioning

confidence: 99%

Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine

2009

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This case study presented herein compares the GIS-based landslide susceptibility mapping methods such as conditional probability (CP), logistic regression (LR), artificial neural networks (ANNs) and support vector machine (SVM) applied in Koyulhisar (Sivas, Turkey). Digital elevation model was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index, stream power index, normalized difference vegetation index, distance from settlements and roads were used in the landslide susceptibility analyses. In the last stage of the analyses, landslide susceptibility maps were produced from ANN, CP, LR, SVM models, and they were then compared by means of their validations. However, area under curve values obtained from all four methodologies showed that the map obtained from ANN model looks like more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results also showed that the CP is a simple method in landslide susceptibility mapping and highly compatible with GIS operating features. Susceptibility maps can be easily produced using CP, because input process, calculation and output processes are very simple in CP model when compared with the other methods considered in this study.

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Section: Introductionmentioning

confidence: 99%

Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine

2009

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“…Spatial maps were produced using a GIS to elaborate deterministic and non-deterministic (probabilistic) models. The probabilistic models were those most frequently used, and a large number of methodologies were developed based on the landslide inventory, geomorphologic assessment, and qualitative and statistical bivariate as well as multivariate analyses (Barredo et al 2000;Brabb et al 1972;Carrara 1983;Carrara et al 1991Carrara et al , 2003Duman et al 2006;Ives and Messerli 1981;Jade and Sarkar 1993;Lamelas et al 2008;Lee and Min 2001;Lee et al 2002Lee et al , 2004Lee 2007;Rengers et al 1998;Rupke et al 1988;Süzen and Doyuran 2004;Tunusluoglu et al 2008;Van Westen 2000;Van Westen and Lulie 2003;Ward et al 1982;Yilmaz and Yildirim 2006).…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya

et al. 2010

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This study considers landslide susceptibility mapping by means of frequency ratio and artificial neural network approaches using geographic information system (GIS) techniques as a basic analysis tool. The selected study area was that of the Panchthar district, Nepal. GIS was used for the management and manipulation of spatial data. Landslide locations were identified from field survey and aerial photographic interpretation was used for location of lineaments. Ten factors in total are related to the occurrence of landslides. Based on the same set of factors, landslide susceptibility maps were produced from frequency ratio and neural network models, and were then compared and evaluated. The weights of each factor were determined using the back-propagation training method. Landslide susceptibility maps were produced from frequency ratio and neural network models, and they were then compared by means of their checking. The landslide location data were used for checking the results with the landslide susceptibility maps. The accuracy of the landslide susceptibility maps produced by the frequency ratio and neural networks is 82.21 and 78.25%, respectively.

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“…However, the failure mechanisms of rock and soil slopes are significantly different. The stability analysis for rock slope is very relevant to the orientation of discontinuities and slope surface (Hoek and Bray 1981;Yilmaz and Yildirim 2006) while infinite slope analysis is usually applied to rainfall-triggered shallow soil slope slides (Liu and Wu 2008). Therefore, it is important to separate the rock type landslides from soil type landslides in preparing landslide inventories.…”

Section: Importance Of Field Investigation For Building a Landslide Imentioning

confidence: 99%

Typical landslides and related mechanisms in Ali Mountain highway induced by typhoon Morakot: perspectives from engineering geology

et al. 2011

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Typhoon Morakot caused serious damage to southern Taiwan. Almost all major mountain highways located in the affected region suffered severe damage from landslides, rock falls, and debris flows. Among these highways, Ali Mountain Highway is one of the most severely affected. Ali Mountain Highway, i.e., T18 route, was constructed in the early 1980s. For the past 30years of operation, it has suffered substantial damage from different natural hazards. The continued damage-rehabilitation cycle is implemented to maintain the serviceability of this highway. Ali Mountain Highway was seriously damaged by the strike of typhoon Morakot on August 8, 2009. Traffic through this highway was interrupted for over 3months until temporary rehabilitation was completed. This paper provides a summary of the damage suffered along the Ali Mountain Highway and presents suggestions for mitigation strategies to minimize the likelihood of similar levels of damage during other extreme typhoons such as the typhoon Morakot. The manuscript summarizes extensive field surveys and aerial photo analysis carried out by the authors to investigate the mechanisms of typical slope failures along the Ali Mountain Highway. The engineering geology perspectives are emphasized in the analyses. Geological and hydrogeological features for the sites in this study were found to be the most influential factors dominating the failures. The fault-related weak zones and impermeable layers dominated the global stability of the slopes. In summary, geotechnical engineers should consider carefully the characteristics of geological structures for mitigation and rehabilitation of landslides. Extreme scenarios, such as typhoon Morakot, should also be considered.

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Structural and geomorphological aspects of the Kat landslides (Tokat—Turkey) and susceptibility mapping by means of GIS

Cited by 40 publications

References 4 publications

Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine

Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine

Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya

Typical landslides and related mechanisms in Ali Mountain highway induced by typhoon Morakot: perspectives from engineering geology

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