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

Abstract

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).

Benchmarking Different SfM-MVS Photogrammetric and iOS LiDAR Acquisition Methods for the Digital Preservation of a Short-Lived Excavation: A Case Study from an Area of Sinkhole Related Subsidence

“…The geological interpretation of the study area has been presented elsewhere [56,57] and the reader is invited to consult the aforementioned work for further details. In summary, the area is characterized by the presence of several types of sinkholes [58,59] with the studied trench being located proximally to the west of a phenomenon that manifested at the surface during the 1970s ( [56] and the references cited therein) and reactivated in the 1980s and 2010s. The presence of this sinkhole is related to a Triassic evaporitic bedrock mantled by variably consolidated and loose Quaternary deposits having variable thickness, which varies in the area, from north to south, from a few meters to more than 60 m [56,60].…”

Natural and human-induced subsidence due to gypsum dissolution: a case study from Inandik, CentralAnatolia, Turkey

“…To detect buried collapse sinkholes and their period of generation, multiple sets of aerial photographs (1953, 1956, 1971, 1990, and 2008), orthophotos and satellite images from 2010 and 2016 were used. The importance of multi-temporal analysis for understanding the evolution process of sinkholes has been stated on several occasions (Delle Rose and Parise, 2002;Festa et al, 2012;Basso et al, 2013;Calligaris et al, 2017).…”

“…Such high solubility leads to problems, the most frequent of which is subsidence, thereby threatening property. The subsidence hazard has been observed in various countries with gypsum karst terrain (Sauro, 1996;Jassim et al, 1997;Cooper, 1998;Paukštys et al, 1999;Cooper and Saunders, 2002;Andrejchuk, 1996, 2002;Delle Rose et al, 2004;Johnson, 2005;Parise and Trocino, 2005;Gutiérrez et al, 2008;Koutepov et al, 2008;Parise et al, 2004Parise et al, , 2009Thierry et al, 2009;Del Prete et al, 2010;Iovine et al, 2010;Cooper and Gutiérrez, 2013;Gutiérrez and Cooper, 2013;Gutiérrez, 2016;Calligaris et al, 2017). In Oviedo (Spain), subsidence due to gypsum dissolution led to the demolition of 362 flats at a loss of 18 million euro (Pando et al, 2012).…”

Preliminary Identification of Geological Hazards from Songpinggou to Feihong in Mao County along the Minjiang River Using SBAS-InSAR Technique Integrated Multiple Spatial Analysis Methods

“…Then, highlow clustering is divided according to the standard normal distribution to highlight the regions with fast motion [36][37][38][39]. Moreover, kernel density analysis is also an effective spatial statistical method, which calculates the density around each point by constructing a smooth surface and realizes the transformation from discrete objects to continuous fields, and then visualizes the abnormal area to make it more prominent [40]. For these abnormal deformation areas, the reliability of the results can be verified by numerical simulation, comparison of GPS monitoring results, or field investigations of the abnormal area [18,41,42].…”