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Sinkhole susceptibility mapping using logistic regression in Karapınar (Konya, Turkey)

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Abstract

Globally, sinkholes cause hundreds of millions of dollars in damage and hundreds of deaths or injuries each year. To mitigate the damage caused by sinkholes, it is necessary to determine the susceptible or hazardous areas. The purpose of this study is to produce a sinkhole susceptibility map based on a logistic regression method within a geographic information system environment. A field survey for this investigation identified the locations of 182 sinkholes in the study area. Many geologic, geomorphologic, hydrogeological and anthropogenic factors that influence sinkhole development were identified in the Karapınar Region. In this study, 30 sinkhole-influencing factors were selected and used in the analysis. The coefficients of the predictor variables were estimated using binary logistic regression analysis and were used to calculate the sinkhole susceptibility for the entire study area. The area value of the receiver operating characteristics curve model was 0.814. The final map indicates that most of the observed sinkholes are predicted in the high or very high sinkhole susceptibility classes. These results indicate that this model is a good estimator of sinkhole susceptibility in the study area. The sinkhole susceptibility map shows that areas with no or very low, low, moderate, high and very high sinkhole susceptibility classes are 605 km2 (25.6 %), 310.8 km2 (13.1 %), 531.2 km2 (22.5 %), 487.7 km2 (20.6 %), and 429.0 km2 (18.1 %), respectively. Interpretation of the susceptibility map shows that sinkhole formation decreased with increasing slope angle, cover thickness, electrical conductivity, and the concentration of calcium, magnesium, sodium, and potassium ions in groundwater. However sinkhole formation increased with drainage density, fault density, upper levels of karstic formations, decline in groundwater level, and well density. This map will serve to help citizens, urban planners and design engineers prevent damage caused by existing sinkholes as well as sinkholes that develop in the future.

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Authors and Affiliations

  1. Department of Civil Engineering, Necmettin Erbakan University, Konya, Turkey

    Adnan Ozdemir

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  1. Adnan Ozdemir
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Correspondence to Adnan Ozdemir.

Electronic supplementary material

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10064_2015_778_MOESM1_ESM.jpg

Online Appendix 1 Input factor maps prepared by using the digital elevation model; (a) elevation, (b) slope c) aspect, (d) drainage line density, (e) distance to drainage lines, and (f) topographic wetness index. Supplementary material 1 (JPEG 2661 kb)

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Online Appendix 2 Input factor maps prepared by using the geological and tectonic features; (a) fault density, (b) distance to fault, (c) cover type, (d) cover thickness, (e) thickness of Insuyu formation, and (f) top level of Insuyu formation. Supplementary material 2 (JPEG 2031 kb)

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Online Appendix 3 Input factor maps prepared by using the geological features and groundwater levels (GWL): (a) Insuyu formation bottom level, (b) GWL in October 1970, (c) GWL in April 2010, (d) GWL in October 2010, (e) differences in GWLs between October 1970 and 2010, (f) differences of GWLs between April and October 2010. Supplementary material 3 (JPEG 2020 kb)

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Online Appendix 4 Input factor maps prepared bu using the groundwater levels (GWLs) and groundwater (GW) chemistry: (a) Ratio of differences of GWLs between 2010 April and October divided to 2010 April, (b) GW gradient in April 2010, (c) Ca concentration in GW, (d) Mg concentration in the GW, (e) Na+K concentration in the GW, (f) SO4 concentration in the GW. Supplementary material 4 (JPEG 6897 kb)

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Online Appendix 5 Input factor maps prepared bu using the groundwater (GW) chemistry and other features: (a) CO3 + HCO3 concentration in GW, (b) total carbonate hardness in GW, (c) pH in GW, (d) well density, (e) land use. Supplementary material 5 (JPEG 6108 kb)

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Ozdemir, A. Sinkhole susceptibility mapping using logistic regression in Karapınar (Konya, Turkey). Bull Eng Geol Environ 75, 681–707 (2016). https://doi.org/10.1007/s10064-015-0778-x

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