Integration of multi-criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy)
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 geo-hazards affecting them. Thi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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University of South Florida Libraries
2017-06-01
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| Series: | International Journal of Speleology |
| Subjects: | |
| Online Access: | http://scholarcommons.usf.edu/ijs/vol46/iss2/6/ |
| _version_ | 1818613928982216704 |
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| author | Chiara Calligaris Stefano Devoto Jorge P. Galve Luca Zini José V. Pérez-Peña |
| author_facet | Chiara Calligaris Stefano Devoto Jorge P. Galve Luca Zini José V. Pérez-Peña |
| author_sort | Chiara Calligaris |
| collection | DOAJ |
| description | 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 geo-hazards 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. Thirty-two 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. |
| first_indexed | 2024-12-16T16:09:55Z |
| format | Article |
| id | doaj.art-fd15aa6f7e0d46c4be7c0da78c25d246 |
| institution | Directory Open Access Journal |
| issn | 0392-6672 1827-806X |
| language | English |
| last_indexed | 2024-12-16T16:09:55Z |
| publishDate | 2017-06-01 |
| publisher | University of South Florida Libraries |
| record_format | Article |
| series | International Journal of Speleology |
| spelling | doaj.art-fd15aa6f7e0d46c4be7c0da78c25d2462022-12-21T22:25:16ZengUniversity of South Florida LibrariesInternational Journal of Speleology0392-66721827-806X2017-06-0146219120410.5038/1827-806X.46.2.2099Integration of multi-criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy)Chiara Calligaris0Stefano Devoto1Jorge P. Galve2Luca Zini3José V. Pérez-Peña4Trieste UniversityTrieste UniversityUniversidad de GranadaTrieste UniversityUniversidad de GranadaThe 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 geo-hazards 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. Thirty-two 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.http://scholarcommons.usf.edu/ijs/vol46/iss2/6/evaporite karstsinkholesusceptibilitynearest neighbourprediction-rate curves |
| spellingShingle | Chiara Calligaris Stefano Devoto Jorge P. Galve Luca Zini José V. Pérez-Peña Integration of multi-criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy) International Journal of Speleology evaporite karst sinkhole susceptibility nearest neighbour prediction-rate curves |
| title | Integration of multi-criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy) |
| title_full | Integration of multi-criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy) |
| title_fullStr | Integration of multi-criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy) |
| title_full_unstemmed | Integration of multi-criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy) |
| title_short | Integration of multi-criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models: the case study of Enemonzo (NE Italy) |
| title_sort | integration of multi criteria and nearest neighbour analysis with kernel density functions for improving sinkhole susceptibility models the case study of enemonzo ne italy |
| topic | evaporite karst sinkhole susceptibility nearest neighbour prediction-rate curves |
| url | http://scholarcommons.usf.edu/ijs/vol46/iss2/6/ |
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