Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy
We conducted a systematic literature review of 105 landslide susceptibility studies in Italy from 1980 to 2023, retrieved from the Scopus database. We discovered that Italian researchers primarily focus on rainfall-induced landslides (86.67% of the articles), especially shallow and fast movements (6...
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MDPI AG
2024-11-01
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| author | Samuele Segoni Rajendran Shobha Ajin Nicola Nocentini Riccardo Fanti |
| author_facet | Samuele Segoni Rajendran Shobha Ajin Nicola Nocentini Riccardo Fanti |
| author_sort | Samuele Segoni |
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| description | We conducted a systematic literature review of 105 landslide susceptibility studies in Italy from 1980 to 2023, retrieved from the Scopus database. We discovered that Italian researchers primarily focus on rainfall-induced landslides (86.67% of the articles), especially shallow and fast movements (60%), with 72% of studies conducted at the local scale, while regional and national-level studies are rare. The most common data sources include remote sensing images validated by field surveys and official data portals at the national or regional level. Data splitting usually follows a 70:30 ratio and 24 modelling techniques were identified, with logistic regression being historically prevalent, although machine learning methods have rapidly gained popularity. Italian studies used 97 predisposing factors, with slope angle (98.09%), lithology (89.52%), land use/land cover (78.09%), and aspect (77.14%) being the most employed. This review also identifies and discusses a few less-used factors, like soil sealing, rainfall, NDVI, and proximity to faults, which showed promising results in experimental studies. Predisposing factors are generally selected by expert judgment, but methods for forward factors selection and collinearity tests are becoming more common. This review synthesizes current knowledge, pinpointing gaps, highlighting emerging methodologies, and suggesting future research directions for better integration of susceptibility studies with landslide risk management. |
| format | Article |
| id | doaj-art-e78ca1d515474324b40d7f6be78bcdb0 |
| institution | Kabale University |
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| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-e78ca1d515474324b40d7f6be78bcdb02024-12-13T16:31:02ZengMDPI AGRemote Sensing2072-42922024-11-011623449110.3390/rs16234491Insights Gained from the Review of Landslide Susceptibility Assessment Studies in ItalySamuele Segoni0Rajendran Shobha Ajin1Nicola Nocentini2Riccardo Fanti3Department of Earth Sciences (DST), University of Florence (UNIFI), Via G. La Pira 4, 50121 Florence, ItalyDepartment of Earth Sciences (DST), University of Florence (UNIFI), Via G. La Pira 4, 50121 Florence, ItalyDepartment of Earth Sciences (DST), University of Florence (UNIFI), Via G. La Pira 4, 50121 Florence, ItalyDepartment of Earth Sciences (DST), University of Florence (UNIFI), Via G. La Pira 4, 50121 Florence, ItalyWe conducted a systematic literature review of 105 landslide susceptibility studies in Italy from 1980 to 2023, retrieved from the Scopus database. We discovered that Italian researchers primarily focus on rainfall-induced landslides (86.67% of the articles), especially shallow and fast movements (60%), with 72% of studies conducted at the local scale, while regional and national-level studies are rare. The most common data sources include remote sensing images validated by field surveys and official data portals at the national or regional level. Data splitting usually follows a 70:30 ratio and 24 modelling techniques were identified, with logistic regression being historically prevalent, although machine learning methods have rapidly gained popularity. Italian studies used 97 predisposing factors, with slope angle (98.09%), lithology (89.52%), land use/land cover (78.09%), and aspect (77.14%) being the most employed. This review also identifies and discusses a few less-used factors, like soil sealing, rainfall, NDVI, and proximity to faults, which showed promising results in experimental studies. Predisposing factors are generally selected by expert judgment, but methods for forward factors selection and collinearity tests are becoming more common. This review synthesizes current knowledge, pinpointing gaps, highlighting emerging methodologies, and suggesting future research directions for better integration of susceptibility studies with landslide risk management.https://www.mdpi.com/2072-4292/16/23/4491ItalylandslidesScopussusceptibilitysystematic review |
| spellingShingle | Samuele Segoni Rajendran Shobha Ajin Nicola Nocentini Riccardo Fanti Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy Remote Sensing Italy landslides Scopus susceptibility systematic review |
| title | Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy |
| title_full | Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy |
| title_fullStr | Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy |
| title_full_unstemmed | Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy |
| title_short | Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy |
| title_sort | insights gained from the review of landslide susceptibility assessment studies in italy |
| topic | Italy landslides Scopus susceptibility systematic review |
| url | https://www.mdpi.com/2072-4292/16/23/4491 |
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