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

Özdemir, Adnan

doi:10.1007/s10064-015-0778-x

Cited by 49 publications

(19 citation statements)

References 79 publications

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“…Nevertheless, our LR model performed similar to the models reported by Ciotoli et al . 24 for sinkhole susceptibility for Lazio Region in central Italy (AUC = 0.779) and by Ozdemir 76 for Karapinar region in Turkey (AUC = 0.814).…”

Section: Discussionmentioning

confidence: 98%

Sinkhole susceptibility mapping in Marion County, Florida: Evaluation and comparison between analytical hierarchy process and logistic regression based approaches

Subedi

Thapa

et al. 2019

Sci Rep

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Sinkholes are the major cause of concern in Florida for their direct role on aquifer vulnerability and potential loss of lives and property. Mapping sinkhole susceptibility is critical to mitigating these consequences by adopting strategic changes to land use practices. We compared the analytical hierarchy process (AHP) based and logistic regression (LR) based approaches to map the areas prone to sinkhole activity in Marion County, Florida by using long-term sinkhole incident report dataset. For this study, the LR based model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.8 compared to 0.73 with the AHP based model. Both models performed better when an independent future sinkhole dataset was used for validation. The LR based approach showed a low presence of sinkholes in the very low susceptibility class and low absence of sinkholes in the very high susceptibility class. However, the AHP based model detected sinkhole presence by allocating more area to the high and very high susceptibility classes. For instance, areas susceptible to very high and high sinkhole incidents covered almost 43.4% of the total area under the AHP based approach, whereas the LR based approach allocated 20.7% of the total area to high and very high susceptibility classes. Of the predisposing factors studied, the LR method revealed that closeness to topographic depression was the most important factor for sinkhole susceptibility. Both models classified Ocala city, a populous city of the study area, as being very vulnerable to sinkhole hazard. Using a common test case scenario, this study discusses the applicability and potential limitations of these sinkhole susceptibility mapping approaches in central Florida.

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Section: Discussionmentioning

confidence: 98%

Sinkhole susceptibility mapping in Marion County, Florida: Evaluation and comparison between analytical hierarchy process and logistic regression based approaches

Subedi

Thapa

et al. 2019

Sci Rep

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“…There is also a desert area in the southern parts of the region characterized by a low precipitation level, annual mean of 265 mm/yr (based on rain record from 1964–2015), which is the lowest in Turkey. Moreover, the evaporation in the region is generally higher than the precipitation [ 38 ]. The Karapinar area has been used for agricultural production for centuries, mostly by dry farming (non-irrigation).…”

Section: Study Areamentioning

confidence: 99%

Land Subsidence and Its Relations with Sinkhole Activity in Karapınar Region, Turkey: A Multi-Sensor InSAR Time Series Study

Orhan

Oliver-Cabrera

Wdowinski

et al. 2021

Sensors

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The Karapinar basin, located in the Central Anatolian part of Turkey, is subjected to land subsidence and sinkhole activity due to extensive groundwater withdrawal that began in the early 2000s. In this study, we use Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite System (GNSS), and groundwater level data to monitor and better understand the relations between groundwater extraction, land subsidence, and sinkhole formation in the Karapinar basin. The main observations used in the study are InSAR-derived subsidence velocity maps calculated from both Sentinel-1 (2014–2018) and COSMO-SkyMed (2016–2017) SAR data. Our analysis reveals broad areas of subsidence with rates exceeding 70 mm/yr. The InSAR-derived subsidence was compared with GNSS data acquired by a continuously operating GNSS station located in the study area, which show a similar rate of subsidence. The temporal characteristic of both InSAR and GNSS time series indicate a long-term subsidence signal superimposed by seasonal variability, which follows the overall groundwater level changes, with over 80% cross-correlation consistency. Our results also indicate that sinkhole activity is limited to slow subsidence areas, reflecting strong cohesion of near-surface rock layers that resist subsidence but yield to collapse in response to aquifer system deformation induced by groundwater extraction.

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“…We quantitatively evaluated and compared the efficiency of the models according to the area under the ROC curve (%). This technique has been applied to assess risk models of various hazards including subsidence [9], landslides [54], and sinkholes [55]; it is a standard method to quantitatively evaluate the quality of probabilistic and statistical models [56]. The x and y axes of the curve are sensitivity and specificity, respectively [56], and the area under the curve ranges from 0.5-1, with higher values indicating higher model accuracy and prediction capability.…”

Section: Model Evaluation and Comparisonmentioning

confidence: 99%

Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models

Syifa

Lee

et al. 2019

Applied Sciences

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To effectively prevent land subsidence over abandoned coal mines, it is necessary to quantitatively identify vulnerable areas. In this study, we evaluated the performance of predictive Bayesian, functional, and meta-ensemble machine learning models in generating land subsidence susceptibility (LSS) maps. All models were trained using half of a land subsidence inventory, and validated using the other half of the dataset. The model performance was evaluated by comparing the area under the receiver operating characteristic (ROC) curve of the resulting LSS map for each model. Among all models tested, the logit boost, which is a meta-ensemble machine leaning model, generated LSS maps with the highest accuracy (91.44%), i.e., higher than that of the other Bayesian and functional machine learning models, including the Bayes net (86.42%), naïve Bayes (85.39%), logistic (88.92%), and multilayer perceptron models (86.76%). The LSS maps produced in this study can be used to mitigate subsidence risk for people and important facilities within the study area, and as a foundation for further studies in other regions.

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

Cited by 49 publications

References 79 publications

Sinkhole susceptibility mapping in Marion County, Florida: Evaluation and comparison between analytical hierarchy process and logistic regression based approaches

Sinkhole susceptibility mapping in Marion County, Florida: Evaluation and comparison between analytical hierarchy process and logistic regression based approaches

Land Subsidence and Its Relations with Sinkhole Activity in Karapınar Region, Turkey: A Multi-Sensor InSAR Time Series Study

Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models

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