Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models
<p>The large-scale landslide susceptibility assessment (LSA) is an important tool for reducing landslide risk through the application of resulting maps in spatial and urban planning. The existing literature more often deals with LSA modelling techniques, and the scientific research very rarely...
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Copernicus Publications
2025-01-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/25/183/2025/nhess-25-183-2025.pdf |
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| author | M. Sinčić S. Bernat Gazibara M. Rossi S. Mihalić Arbanas |
| author_facet | M. Sinčić S. Bernat Gazibara M. Rossi S. Mihalić Arbanas |
| author_sort | M. Sinčić |
| collection | DOAJ |
| description | <p>The large-scale landslide susceptibility assessment (LSA) is an important tool for reducing landslide risk through the application of resulting maps in spatial and urban planning. The existing literature more often deals with LSA modelling techniques, and the scientific research very rarely focuses on acquiring relevant thematic and landslide data, necessary to achieve reliable results. Therefore, the paper focuses on the crucial step of classifying continuous landslide conditioning factors for susceptibility modelling by presenting an innovative comprehensive analysis that resulted in 54 landslide susceptibility models to test 11 classification criteria (scenarios which vary from stretched values, partially stretched classes, heuristic approach, classification based on studentized contrast and landslide presence, and commonly used classification criteria, such as natural neighbour, quantiles and geometrical intervals) in combination with 5 statistical methods. The large-scale landslide susceptibility models were derived for small and shallow landslides in the pilot area (21 km<span class="inline-formula"><sup>2</sup></span>) located in the City of Zagreb (Croatia), which occur mainly in soils and soft rocks. Some of the novelties in LSA are the following: scenarios using stretched landslide conditioning factor values or classification with more than 10 classes prove more reliable; certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others; all the tested machine learning methods give the best landslide susceptibility model performance using continuous stretched landslide conditioning factors derived from high-resolution input data. The research highlights the importance of qualitative assessments, alongside commonly used quantitative metrics, to verify spatial accuracy and to test the applicability of derived landslide susceptibility maps for spatial planning purposes.</p> |
| format | Article |
| id | doaj-art-a26a45d15d6f4d4f9b7070dfc9d4dac1 |
| institution | Kabale University |
| issn | 1561-8633 1684-9981 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj-art-a26a45d15d6f4d4f9b7070dfc9d4dac12025-01-07T14:34:13ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812025-01-012518320610.5194/nhess-25-183-2025Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility modelsM. Sinčić0S. Bernat Gazibara1M. Rossi2S. Mihalić Arbanas3Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, 10000 Zagreb, CroatiaFaculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, 10000 Zagreb, CroatiaConsiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, 06128 Perugia, ItalyFaculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia<p>The large-scale landslide susceptibility assessment (LSA) is an important tool for reducing landslide risk through the application of resulting maps in spatial and urban planning. The existing literature more often deals with LSA modelling techniques, and the scientific research very rarely focuses on acquiring relevant thematic and landslide data, necessary to achieve reliable results. Therefore, the paper focuses on the crucial step of classifying continuous landslide conditioning factors for susceptibility modelling by presenting an innovative comprehensive analysis that resulted in 54 landslide susceptibility models to test 11 classification criteria (scenarios which vary from stretched values, partially stretched classes, heuristic approach, classification based on studentized contrast and landslide presence, and commonly used classification criteria, such as natural neighbour, quantiles and geometrical intervals) in combination with 5 statistical methods. The large-scale landslide susceptibility models were derived for small and shallow landslides in the pilot area (21 km<span class="inline-formula"><sup>2</sup></span>) located in the City of Zagreb (Croatia), which occur mainly in soils and soft rocks. Some of the novelties in LSA are the following: scenarios using stretched landslide conditioning factor values or classification with more than 10 classes prove more reliable; certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others; all the tested machine learning methods give the best landslide susceptibility model performance using continuous stretched landslide conditioning factors derived from high-resolution input data. The research highlights the importance of qualitative assessments, alongside commonly used quantitative metrics, to verify spatial accuracy and to test the applicability of derived landslide susceptibility maps for spatial planning purposes.</p>https://nhess.copernicus.org/articles/25/183/2025/nhess-25-183-2025.pdf |
| spellingShingle | M. Sinčić S. Bernat Gazibara M. Rossi S. Mihalić Arbanas Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models Natural Hazards and Earth System Sciences |
| title | Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models |
| title_full | Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models |
| title_fullStr | Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models |
| title_full_unstemmed | Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models |
| title_short | Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models |
| title_sort | comparison of conditioning factor classification criteria in large scale statistically based landslide susceptibility models |
| url | https://nhess.copernicus.org/articles/25/183/2025/nhess-25-183-2025.pdf |
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