Sinkhole susceptibility, Lazio Region, central Italy

Ciotoli, Giancarlo; Loreto, E. Di; Finoia, María Grazia; Liperi, Lorenzo; Meloni, Fabio; Nisio, Stefania; Sericola, A.

doi:10.1080/17445647.2015.1014939

Cited by 22 publications

(32 citation statements)

References 10 publications

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“…In this framework, the SIM is an essential tool for local authorities for future territorial planning in order to avoid hazardous areas. Furthermore, the analysis of the SIM can motivate regional authorities to produce Sinkhole Susceptibility Maps (Ciotoli et al, 2016;Galve et al, 2008Galve et al, , 2009bGalve et al, , 2011 where phenomena are common and/or recurrent being a fundamental tool for sinkhole geo-hazard assessment and their forecasting.…”

Section: Discussionmentioning

confidence: 99%

Evaporite sinkholes of the Friuli Venezia Giulia region (NE Italy)

2017

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Sinkholes are common in the Friuli Venezia Giulia (FVG) Region (NE Italy), where the presence of karstifiable rocks favours their occurrence accelerated by intense rainfalls. Their existence has been reported since the end of the 1800s along the Tagliamento Valley, in correspondence with the mantled evaporites (gypsum). Furthermore, tens of evaporite sinkholes have been documented on the reliefs adjacent to the village of Sauris and along the narrow W-Eoriented valleys, where regional faults have played a major role in their spatial distribution. This paper reports for the first time an inventory of the sinkholes affecting the evaporites of the FVG Region. These phenomena were mapped and categorised using a genetic classification. The main output is an A0-format map, which incorporates a 1:50,000 scale Sinkhole Inventory Map (SIM). The SIM encompasses 552 sinkholes. The cover suffosion sinkholes are the most abundant, followed by bedrock collapses. There is a clear prevalence of the circular shape (65%) over other shapes. Diameters are 1-140 m, with depths ranging 0.1-40 m with a mean value of 4.5 m. The SIM can motivate regional planning authorities to perform further investigations aimed to understand the geomorphological evolutions of these phenomena. ARTICLE HISTORY

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Section: Discussionmentioning

confidence: 99%

Evaporite sinkholes of the Friuli Venezia Giulia region (NE Italy)

2017

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“…This method may not be practical in situations where underlying geological feature constraining sinkhole formation is highly directional (e.g., linear) 23 . Probabilistic methods use a statistical relationship between predisposing factors and the existing events of sinkholes to map sinkhole susceptible zones 8,9,24 . Previous studies of susceptibility mapping of natural hazards have commonly used bivariate and multivariate statistical analysis 25–28 .…”

Section: Introductionmentioning

confidence: 99%

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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“…Sinkhole susceptibility maps (SSMs) developed and validated through statistical approaches provide a spatially continuous and easily accessible tool for managing sinkhole hazards (Galve et al, 2009). Recently, several statistical approaches have been applied to the development of SSMs in a Geographic Information System environment, including analytic hierarchy processes (Taheri et al, 2015), frequency ratio (Yilmaz, 2007), logistic regression (Ciotoli et al, 2016;Papadopoulou-Vrynioti, Bathrellos, Skilodimou, Kaviris, & Makropoulos, 2013), artificial neural networks (Yilmaz, Marschalko, & Bednarik, 2013), and conditional probability (Yilmaz et al, 2013). These methods provide susceptibility assessments that are objective and reproducible and may reach a high spatial resolution (Chen, Li, Wang, Chen, & Liu, 2014).…”

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confidence: 99%

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

Taheri¹,

Shahabi

Chapi

et al. 2019

Land Degrad Dev

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Land 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.

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Sinkhole susceptibility, Lazio Region, central Italy

Cited by 22 publications

References 10 publications

Evaporite sinkholes of the Friuli Venezia Giulia region (NE Italy)

Evaporite sinkholes of the Friuli Venezia Giulia region (NE Italy)

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

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

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