GIS based susceptibility mapping of karst depression in gypsum: A case study from Sivas basin (Turkey)

Yilmaz, Işık

doi:10.1016/j.enggeo.2006.12.004

Cited by 127 publications

(68 citation statements)

References 7 publications

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“…FR is the expression of the ratio of the probability of an occurrence to that of a non-occurrence for any attribute (Lee and Sambath 2006;Lee and Pradhan 2007). It is regarded as a simple, easily understandable method (Yilmaz 2007). The greater the FR, the more substantial is the relationship between occurrence and specific variable (Pradhan 2010b;Sujatha et al 2013).…”

Section: Methodsmentioning

confidence: 99%

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Tehrany

Shabani

Jebur

et al. 2017

Geomatics, Natural Hazards and Risk

214

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The aim of this research was to evaluate the predictive performances of frequency ratio (FR), logistic regression (LR) and weight of evidence (WoE), in flood susceptibility mapping in China. In addition, the ensemble WoE and LR and ensemble FR and LR techniques were applied and used in the evaluation. The flood inventory map, consisting of 196 flood locations, was extracted from a number of sources. The flood inventory data were randomly divided into a testing data-set, allocating 70% for training, and the remaining 30% for validation. The 15 flood conditioning factors included in the spatial database were altitude, slope, aspect, geology, distance from river, distance from road, distance from fault, soil type, land use/cover, rainfall, Normalized Difference Vegetation Index, Stream Power Index, Topographic Wetness Index, Sediment Transport Index and curvature. For validation, success and prediction rate curves were developed using area under the curve (AUC) method. The results indicated that the highest prediction rate of 90.36% was achieved using the ensemble technique of WoE and LR. The standalone WoE produced the highest prediction rate among the individual methods. It can be concluded that WoE offers a more advanced method of mapping prone areas, compared with the FR and LR methods.

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Section: Methodsmentioning

confidence: 99%

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

Tehrany

Shabani

Jebur

et al. 2017

Geomatics, Natural Hazards and Risk

214

View full text Add to dashboard Cite

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“…This type of information can then be analyzed with respect to other parameters using GIS techniques to produce susceptibility and hazard models (Galve et al, 2008(Galve et al, , 2009b. Yilmaz (2007) working in the Sivas gypsum karst, Turkey, considered multiple parameters related to the topography, geology, hydrology, human structures and vegetation to calculate a susceptibility map. In the Paris area, using topographical and geotechnical data, 3D geological modeling and probability analyses in a GIS environment, Thierry et al (2009) determined three probabilities as GIS layers: 1.…”

Section: Surveying Sinkhole Susceptibility Gis and Planningmentioning

confidence: 99%

6.35 Dealing with Gypsum Karst Problems: Hazards, Environmental Issues, and Planning

Cooper

Gutiérrez

2013

Treatise on Geomorphology

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Synopsis/Abstract 153 wordsGypsum dissolves rapidly underground and at the surface forming gypsum karst features that include caves, subsidence areas and sinkholes. Mapping these landforms, understanding the gypsum karst and local hydrogeology, and producing sinkhole susceptibility and hazard maps are crucial for development and public safety. Situations that change the local hydrogeology, such as dams, water abstraction or injection/drainage, can accelerate dissolution and subsidence processes increasing the severity of the problems; dams and canals built on gypsum karst can leak or fail catastrophically. Gypsum karst problems can be mitigated by careful surveying and scientific investigation followed by phased preventive planning, ground investigation and construction incorporating sinkhole-proof designs. Towns and cities, including parts of Paris (France), Dzerzhinksk (Russia), Madrid and Zaragoza (Spain), Birzai (Lithuania) and Ripon and Darlington (UK), are developed on such ground requiring local planning guidelines and special construction methods. Roads, railways, pipelines and bridges are particularly vulnerable to such subsidence and require special consideration.

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“…Probabilistic and statistical methods have been extensively applied in landslide susceptibility analyses (e.g., Calligaris et al, 2013;Petschko et al, 2014;Piacentini et al, 2015;Steger et al, 2016), but they are not very common in the literature related to karst subsidence sinkholes (see Table 1). The evaporite karst of the Ebro Valley (Spain) is the region where these methods have often been tested (Simón et al, 1991;Soriano & Simón, 1995;Simón & Soriano, 2002;Lamelas et al, 2008;Galve et al, 2008Galve et al, , 2009bGalve et al, , 2011, but other authors also used these techniques to produce sinkhole susceptibility maps in other study areas (Hyland, 2005;Yilmaz, 2007;Dai et al, 2008;Doctor et al, 2008;Oh & Lee, 2010;Nachbaur & Rohmer, 2011;Doctor & Doctor, 2012;Papadopoulou-Vrynioti et al, 2013;Pradhan et al, 2014;Cahalan, 2015;D'Angella et al, 2015;Ciotoli et al, 2016; Neural Networks and Weights of Evidence. The present paper describes the application of a method based on the Favorability Functions approach (Chung & Fabbri, 1993), and the Likelihood Ratio (Chung, 2006).…”

Section: Introductionmentioning

confidence: 99%

Integration of multi-criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy)

Calligaris¹,

Devoto²,

Galvé³

et al. 2017

IJS

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Abstract:The significance of intra-mountain valleys to infrastructure and human settlements and the need to mitigate the geo-hazard affecting these assets are fundamental to the economy of Italian alpine regions. Therefore, there is a real need to recognize and assess possible geohazards affecting them. This study proposes the use of GIS-based analyses to construct a sinkhole susceptibility model based on conditioning factors such as land use, geomorphology, thickness of shallow deposits, distance to drainage network and distance to faults. Thirtytwo models, applied to a test site (Enemonzo municipality, NE Italy), were produced using a method based on the Likelihood Ratio (λ) function, nine with only one variable and 23 applying different combinations. The sinkhole susceptibility model with the best forecast performance, with an Area Under the Prediction Rate Curve (AUPRC) of 0.88, was that combining the following parameters: Nearest Sinkhole Distance (NSD), land use and thickness of the surficial deposits. The introduction of NSD as a continuous variable in the computation represents an important upgrade in the prediction capability of the model. Additionally, the model was refined using a kernel density estimation that produced a significant improvement in the forecast performance.evaporite karst, sinkhole, susceptibility, nearest neighbour, prediction-rate curves Calligaris C., Devoto S., Galve J.P., Zini L. and Pérez-Peña, J.V., 2017. Integration of multicriteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy).

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GIS based susceptibility mapping of karst depression in gypsum: A case study from Sivas basin (Turkey)

Cited by 127 publications

References 7 publications

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques

6.35 Dealing with Gypsum Karst Problems: Hazards, Environmental Issues, and Planning

Integration of multi-criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy)

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