Sinkhole Susceptibility Mapping Research Articles

GIS-based sinkhole susceptibility mapping using the best worst method

GIS-based sinkhole susceptibility mapping using the best worst method

Machine learning for sinkhole risk mapping in Guidonia-Bagni di Tivoli plain (Rome), Italy

This work presents a sinkhole susceptibility and risk assessment mapping in Guidonia-Bagni di Tivoli plain (Italy), a travertine sinkhole-prone area where sudden occurrences of sinkholes have happened in past and recent times. We collected a point-like sinkhole inventory and we considered a series of different sinkhole-controlling and precursory factors over the study area, related to its geo-litho-hydrological setting and to its terrain deformational scenario, i.e. ground motion rates derived from InSAR COSMO-SkyMed imagery. A sinkhole susceptibility map was produced through a machine learning model, namely Maximum Entropy algorithm (MaxEnt). Results highlight that the most determining factors for sinkhole formation are the lithology, the travertine thickness, groundwater and the land use. The sinkhole susceptibility map was then combined with data on vulnerability and elements-at-risk economic exposure in order to provide a sinkhole risk map of the area. The outcomes show that areas at higher risk covers about 2% of the total study area and primarily relies on the zoning of the main urban fabric. In particular, it is worth to highlight that 5% of the whole road-network pavement and 27% of all the residential buildings fall into High and Very High risk classes. Overall, results of this work demonstrate capabilities of machine learning models to assess sinkhole susceptibility for predicting potential sinkhole areas, and provide a sinkhole risk map, along with information on urban environment, as a useful tool for urban planning and geohazard risk management.

Probabilistic spatial susceptibility modeling of carbonate karst sinkhole

Sinkhole is one of major geohazards in karst area where soluble carbonate bedrock is underlain by cover soils (e.g., sandy or clayey soils) and active groundwater circulation is present. Groundwater recharge easily erode the overburden soils away into cavities in the bedrock, and ultimately structural collapse in ground. The induced ground collapse/subsidence may pose a great threat to human safety, environment, and buildings and infrastructure. The goal of this study is to develop a method/tool to spatially assess the possibility of sinkhole occurrence. The paper presents two main taks: (1) development of a probabilistic spatial prediction model of sinkhole susceptibility and (2) a geographical information system (GIS)-based regional-scale sinkhole susceptibility map. The study area is the east central Florida (ECF) region that has been experiencing more abrupt and larger cover collapse sinkholes. The research methodology employs two statistical methods for the spatial prediction model, frequency ratio (FR) and logistic regression (LR), accounting for six criteria that are soil erosion, mechanical stability, distance, geology, human activity, and climate condition. The final section of factors includes head difference, soil permeability, thicknesses of aquifer systems (overburden, surficial/intermediate aquifer), distance to karst features, surficial geology, lithology, land use/land cover, and rainfall. Once the models were constructed, both models were validated and compared by using receiver operating characteristics (ROC) analysis. The results indicate that the LR model better delineates high susceptibility areas of sinkhole and hydrological factors (recharge and head difference) are more accurately reflected on the model of the LR.

Maximum entropy model-based spatial sinkhole occurrence prediction in Karapınar, Turkey

Sinkholes in Karapınar and their rapidly increasing occurrence rate are considered one of the main hazards that threaten arable lands and human life. The sudden occurrence and unavoidable characteristics of sinkholes make them more dangerous and challenging to avoid. More than 300 sinkholes have been recorded in the Karapınar region of Konya province in Turkey. There are intensive agricultural activities in the region, and therefore over 60,000 water wells are used to meet the demand. Thus, drought, the effects of climate change and decreasing precipitation rate reveal stress on sinkhole occurrence due to the geological structure of the region and its high tendency to sinkholes since ancient times due to its volcanic history. The primary purpose of this study is to predict possible sinkhole occurrence probabilities in Konya, Karapınar region based on historical occurrences and to report to the authorities to raise awareness about this problem. The Maximum Entropy (MaxEnt) model is applied for sinkhole susceptibility mapping by evaluating 17 variables affecting sinkhole occurrence in meteorological, topographic, environmental, and geological aspects. The results indicated that 458.52 km2 (2.48%) of the study area is highly susceptible to sinkholes. 100 sinkholes were assigned as sample data, and 45 sinkholes were set as test data for the MaxEnt model. The AUC values of training data with 0.978 and test data with 0.963 were calculated where a good correlation was provided. The variables Annual Mean Temperature, Precipitation Seasonality (Coefficient of Variation) Geology, and precipitation, which are mostly responsible for sinkhole formations, have been calculated.

Integration of an InSAR and ANN for Sinkhole Susceptibility Mapping: A Case Study from Kirikkale-Delice (Turkey)

Suitable route determination for linear engineering structures is a fundamental problem in engineering geology. Rapid evaluation of alternative routes is essential, and novel approaches are indispensable. This study aims to integrate various InSAR (Interferometric Synthetic Aperture Radar) techniques for sinkhole susceptibility mapping in the Kirikkale-Delice Region of Turkey, in which sinkhole formations have been observed in evaporitic units and a high-speed train railway route has been planned. Nine months (2019–2020) of ground deformations were determined using data from the European Space Agency’s (ESA) Sentinel-1A/1B satellites. A sinkhole inventory was prepared manually using satellite optical imagery and employed in an ANN (Artificial Neural Network) model with topographic conditioning factors derived from InSAR digital elevation models (DEMs) and morphological lineaments. The results indicate that high deformation areas on the vertical displacement map and sinkhole-prone areas on the sinkhole susceptibility map (SSM) almost coincide. InSAR techniques are useful for long-term deformation monitoring and can be successfully associated in sinkhole susceptibility mapping using an ANN. Continuous monitoring is recommended for existing sinkholes and highly susceptible areas, and SSMs should be updated with new results. Up-to-date SSMs are crucial for the route selection, planning, and construction of important transportation elements, as well as settlement site selection, in such regions.

An application on sinkhole susceptibility mapping by integrating remote sensing and geographic information systems

Karapinar, located in the middle of Konya Closed Basin in Turkey, is a district that is agriculturally and economically significant. The natural life and agricultural future of the district are threatened by the gradual reduction of underground water resources, application of intensive agricultural activities that are unable to account for climate characteristics, and climate change effects. When the geological structure of the district is added to all these parameters, environmental problems such as land subsidence and sinkhole formation occur in the district. This study presents the results of a research project evaluating sinkhole susceptibility using remote sensing and geographic information systems in the district of Karapinar in Konya, Turkey. In addition, the importance of adding deformation map that was produced using InSAR analyses as a factor in the sinkhole susceptibility mapping was emphasized. A sinkhole susceptibility map was produced to determine the potential sinkhole areas based on expert opinions by applying the analytical hierarchy process approach and considering a selection of 13 factors. The map produced was in high agreement with the recent sinkholes in the district. The results of this study showed that the analytical hierarchy process can be a useful approach for susceptibility assessments when data on the main controlling factors have sufficient accuracy and spatial coverage. In addition, the map presented apparent sinkhole susceptibility in the area in accordance with the selected parameters and informed decision makers regarding natural hazard risks in Karapinar.

Development of Sinkhole Susceptibility Map of East Central Florida

AbstractThis paper presents the implementation of the frequency ratio (FR) method to evaluate the contribution of every factor on sinkholes and develop a sinkhole susceptibility map for East Centra...

Sinkhole Detection and Characterization Using LiDAR-Derived DEM with Logistic Regression

Depressions due to sinkhole formation cause significant structural damages to buildings and civil infrastructure. Traditionally, visual inspection has been used to detect sinkholes, which is a subjective way and time- and labor-consuming. Remote sensing techniques have been introduced for morphometric studies of karst landscapes. This study presents a methodology for the probabilistic detection of sinkholes using LiDAR-derived digital elevation model (DEM) data. The proposed study provides benefits associated with: (1) Detection of unreported sinkholes in rural and/or inaccessible areas, (2) automatic delineation of sinkhole boundaries, and (3) quantification of the geometric characteristics of those identified sinkholes. Among sixteen morphometric parameters, nine parameters were chosen for logistic regression, which was then employed to compute the probability of sinkhole detection; a cutoff value was back-calculated such that the sinkhole susceptibility map well predicted the reported sinkhole boundaries. According to the results of the LR model, the optimal cutoff value was calculated to be 0.13, and the area under the curve (AUC) of the receiver operating characteristic curve (ROC) was 0.90, indicating the model is reliable for the study area. For those identified sinkholes, the geometric characteristics (e.g., depth, length, area, and volume) were computed.

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

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.

Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms

AbstractLand degradation has been recognized as one of the most adverse environmental impacts during the last century. The occurrence of sinkholes is increasing dramatically in many regions worldwide contributing to land degradation. The rise in the sinkhole frequency is largely due to human‐induced hydrological alterations that favour dissolution and subsidence processes. Mitigating detrimental impacts associated with sinkholes requires understanding different aspects of this phenomenon such as the controlling factors and the spatial distribution patterns. This research illustrates the development and validation of sinkhole susceptibility models in Hamadan Province, Iran, where a large number of sinkholes are occurring under poorly understood circumstances. Several susceptibility models were developed with a training sample of sinkholes, a number of conditioning factors, and four different statistical approaches: naïve Bayes, Bayes net (BN), logistic regression, and Bayesian logistic regression. Ten conditioning factors were initially considered. Factors with negligible contribution to the quality of predictions, according to the information gain ratio technique, were discarded for the development of the final models. The validation of susceptibility models, performed using different statistical indices and receiver operating characteristic curves, revealed that the BN model has the highest prediction capability in the study area. This model provides reliable predictions on the future distribution of sinkholes, which can be used by watershed and land use managers for designing hazard and land‐degradation mitigation plans.

Using Gis-based Spatial Analysis To Determine Factors Influencing the Formation of Sinkholes in Greene County, Missouri

Abstract Sinkholes are inherent features of the karst terrain of Greene County, Missouri, that present hazards and engineering challenges to construction/infrastructure development. Analysis of relationships between the spatial distribution of sinkholes and possible influencing factors can help in understanding the controls involved in the formation of sinkholes. The spatial analysis outlined herein can aid in the assessment of potential sinkhole hazards. In this research, Geographic Information System–based ordinary least squares regression (OLS) and geographically weighted regression (GWR) methods were used to determine and evaluate principal factors appearing to influence the formation and distribution of karst sinkholes. From the OLS result, seven out of 12 possible influencing factors were found to exert significant control on sinkhole formation processes in the study area. These factors are overburden thickness, depth to groundwater, slope of the ground surface, distance to the nearest surface drainage line, distance to the nearest geological structure (such as faults or folds), distance to the nearest road, and distance to the nearest spring. These factors were then used as independent variables in the GWR model. The GWR model examined the spatial non-stationarity among the various factors and demonstrated better performance over OLS. GWR model coefficient estimates for each variable were mapped. These maps provide spatial insights into the influence of the variables on sinkhole densities throughout the study area. GWR spatial analysis appears to be an effective approach to understand sinkhole-influencing factors. The results could be useful to provide an objective means of parameter weighting in models of sinkhole susceptibility or hazard mapping.

SINKHOLE SUSCEPTIBILITY ANALYSIS FOR KARAPINAR/KONYA VIA MULTI CRITERIA DECISION

Abstract. Sinkholes are being a natural hazard which threads economic and human life. Sudden occurrence characteristic of sinkholes make it unable to escape. There are a lot of factor that activate sinkholes such as geology, irrigation, land use and human related factors. In Karapınar, Konya, there are over 200 sinkholes and this count is getting increased in recent years. Especially active agricultural lands, decreasing ground water level, extreme irrigation by 55267 water wells increase the risk factor of Karapınar. Nowadays, considering the economic contribution of Karapınar to Turkey economy in the field of agriculture, solar energy fields and thermal reactor which will be planned in next few years, prediction of sinkholes and searching for preventation ways are being more important issue. In this study, sinkhole susceptibility map via AHP was carried out for Karapınar in Konya. Slope, land use, elevation, geology, water wells, distance to roads and settlements criteria are included to determine susceptibility. The weights are calculated with AHP for each criterion and generated susceptibility map is overlapped with existing sinkholes. Suggestions and results are shared for this study.

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

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 Karapinar 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.

Sinkhole susceptibility, Lazio Region, central Italy

Lazio is a region particularly affected by catastrophic sinkhole collapse. For some areas sinkholes appear to be sporadic, whilst other areas are particularly affected by sinkholes. These events can cause considerable damage when they occur in an urban area or in areas with transport networks. For several years, the Lazio Region has conducted studies on sinkholes in order to define areas prone to this phenomenon. This study involved the use of cartographic sources, historical documentation, as well as field trips, in order to construct an inventory of sinkhole cases in the region, as well as to identify sinkhole prone areas (SPAs). The inventory allowed each sinkhole case to be analysed within the geological and hydrogeological setting, as well as the surrounding vegetation and degree of urbanisation, in which it occurred. Correlations were also performed with tectonic and structural trends, and with triggering factors, such as earthquakes. Some predisposing variables to sinkhole phenomena are then selected. The application of multivariate logistic regression and geospatial analyses allows the selection of the most significant factors, as well as their optimal weights used to construct a sinkhole susceptibility map of the Latium Region at the 1:250,000 scale. The identification of the susceptibility grade of SPAs will help in the management of these areas, based on the risk level.

Sinkhole susceptibility mapping using the analytical hierarchy process (AHP) and magnitude–frequency relationships: A case study in Hamadan province, Iran

Since 1989, an increasing number of sinkhole occurrences have been reported in the Kabudar Ahang and Razan–Qahavand subcatchments (KRQ) of Hamadan province, western Iran. The sinkhole-related subsidence phenomenon poses a significant threat for people and human structures, including sensitive facilities like the Hamadan Power Plant. Groundwater over-exploitation from the thick alluvial cover and the underlying cavernous limestone has been identified as the main factor involved in sinkhole development. A sinkhole susceptibility model was produced in a GIS environment applying the analytical hierarchy process (AHP) approach and considering a selection of eight factors, each categorized into five classes: distance to faults (DF), water level decline (WLD), groundwater exploitation (GE), penetration of deep wells into karst bedrock (PKA), distance to deep wells (DDW), groundwater alkalinity (GA), bedrock lithology (BL), and alluvium thickness (AT). Relative weights were preliminarily assigned to each factor and to their different classes through systematic pairwise comparisons based on expert judgment. The resulting sinkhole susceptibility index (SSI) values were then classified into four susceptibility classes: low, moderate, high and very high susceptibility. Subsequently, the model was refined through a trial and error process involving changes in the relative weights and iterative evaluation of the prediction capability. Independent evaluation of the final model indicates that 55% and 45% of the subsidence events fall within the very high and high, susceptibility zones, respectively. The results of this study show that AHP can be a useful approach for susceptibility assessment if data on the main controlling factors have sufficient accuracy and spatial coverage. The limitations of the model are partly related to the difficulty of gathering data on some important geological factors, due to their hidden nature. The magnitude and frequency relationship constructed with the 41 sinkholes with chronological and morphometric data indicates maximum recurrence intervals of 1.17, 2.14 and 4.18years for sinkholes with major axial lengths equal to or higher than 10m, 20m, and 30m, respectively.

Sinkhole Susceptibility Mapping Using a Frequency Ratio Method and GIS Technology Near Karapınar, Konya-Turkey

The purpose of this study is to produce a sinkhole susceptibility map of the Obruk Plateau in central Turkey, based on a frequency ratio method within a Geographic Information System environment. Using field surveys, the locations of the 182 sinkholes were determined in the study area. In this study, 30 sinkhole-related factors were determined and used in the analysis. The values of the frequency ratio were estimated using frequency ratio analysis and were used to calculate the sinkhole potential for the entire study area. For model validation, the constructed sinkhole occurrence potential map was overlaid onto the 182 observed sinkholes, showing that 169 sinkholes (93% of all sinkholes) were determined to be in the high and moderate sinkhole occurrence potential areas. In addition, the predictive capabilities of the models were determined by the area under the relative operating characteristic (ROC) curve. The area value under the ROC curve was 0.953. These results indicate that the model is a good estimator of the sinkhole potential in the study area. The sinkhole potential map shows that the areas of very low or no, low, moderate and high sinkhole potential classes are 712.31 km2; 641.15 km2, 572.45 km2; and 438.01 km2, respectively. The interpretations of the potential map showed that geology (lithology), elevation, aspect, fault density, distance to fault, and decline of the groundwater level play major roles in sinkhole occurrence and distribution in the Obruk Plateau. The resulting sinkhole potential map can aid planners and engineers in development plans and land-use planning.

The effect of groundwater overexploitation on land subsidence and sinkhole occurrences, western Iran

The main objective of this study was to evaluate the relationship between the declining groundwater level and land subsidence and sinkhole collapse in the Hamedan central plain, western Iran. In the last three decades, the increase in the use of groundwater for agricultural and industrial expansion accompanied by successive droughts has greatly lowered the groundwater level in the area. This has resulted in land subsidence and increased sinkhole occurrence. In this paper, both hydrogeological and geotechnical investigations were performed to identify a mechanism to explain the changes in the occurrence of these geological hazards. This study identifies three main triggering factors for the development of land subsidence and sinkhole formation. These factors include the presence of fine-grained materials (for land subsidence) and karstic limestone bedrock (for sinkhole formation), increase of effective stress, and generation of turbulent groundwater flow at the soil–bedrock interface. Risk of the occurrence of sinkholes affects planning decisions for most types of development in the study area. As a result of this study a sinkhole susceptibility map was generated indicating the location of hazardous regions.

Evaluating and comparing methods of sinkhole susceptibility mapping in the Ebro Valley evaporite karst (NE Spain)

Multiple sinkhole susceptibility models have been generated in three study areas of the Ebro Valley evaporite karst (NE Spain) applying different methods (nearest neighbour distance, sinkhole density, heuristic scoring system and probabilistic analysis) for each sinkhole type separately (cover collapse sinkholes, cover and bedrock collapse sinkholes and cover and bedrock sagging sinkholes). The quantitative and independent evaluation of the predictive capability of the models reveals that: (1) The most reliable susceptibility models are those derived from the nearest neighbour distance and sinkhole density. These models can be generated in a simple and rapid way from detailed geomorphological maps. (2) The reliability of the nearest neighbour distance and density models is conditioned by the degree of clustering of the sinkholes. Consequently, the karst areas in which sinkholes show a higher clustering are a priori more favourable for predicting new occurrences. (3) The predictive capability of the best models obtained in this research is significantly higher (12.5–82.5%) than that of the heuristic sinkhole susceptibility model incorporated into the General Urban Plan for the municipality of Zaragoza. Although the probabilistic approach provides lower quality results than the methods based on sinkhole proximity and density, it helps to identify the most significant factors and select the most effective mitigation strategies and may be applied to model susceptibility in different future scenarios.

Development and morphometry of sinkholes in coastal plains of Apulia, southern Italy. Preliminary sinkhole susceptibility assessment

Evolution of coastlines in karst areas may be strongly controlled by dissolution processes which favour the development of surface and subsurface landforms. The generation of caves in these environments is commonly favoured by the mixing between fresh and brackish waters. The sinkholes resulting from the upward propagation of the caves may interfere with the anthropogenic environment and cause damage to human elements (property and activities). To highlight the often underestimated importance of karst phenomena in coastal areas, we have analyzed a coastal stretch of Apulia, in southern Italy. The study area, covering an extension of about 6 km 2, is situated in the Ionian coast, and presents several interesting karst landforms that are generally connected to caves. Tens of sinkholes were mapped through field surveys, multi-year aerial-photographs (dating back to the 1940s) and archival research. We have performed a morphometric analysis of the sinkholes. The analysis describes the main parameters of the sinkholes (area, length, width, and depth), and the control exerted by the main discontinuity systems in the area. The detrimental effects derived from interaction between human environment and these karst landforms is also under consideration. A sinkhole susceptibility map, which may provide useful information for planners, developers and the insurance industry has eventually been produced through application of a decision tree model.