Optimal slope units partitioning in landslide susceptibility mapping
Dados Bibliográficos
| ID | 16840663 |
|---|---|
| Autores | Chiara Martinello (0000-0001-9527-487X, University of Palermo), Chiara Cappadonia (0000-0002-5119-3004, University of Palermo), Christian Conoscenti (0000-0002-7974-7961, University of Palermo, autor correspondente), Valerio Agnesi (0000-0002-2422-6720, University of Palermo), Edoardo Rotigliano (0000-0002-1072-3160, University of Palermo) |
| Ano | 2021 |
| Volume | 17 |
| Fascículo | 3 |
| Páginas | 152-162 |
| Data de publicação | 2021-06-01 |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | Journal of Maps (JOURNAL) |
| Identificadores do periódico | ISSN: 1744-5647 • E-ISSN: 1744-5647 |
| Editora | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/17445647.2020.1805807 |
| OpenAlex | W3081482964 |
| Idioma | EN |
| Citações recebidas | 4 |
| Referências citadas | 33 |
In landslide susceptibility modeling, the selection of the mapping units is a very relevant topic both in terms of geomorphological adequacy and suitability of the models and final maps. In this paper, a test to integrate pixels and slope units is presented. MARS (Multivariate Adaptive Regression Splines) modeling was applied to assess landslide susceptibility based on a 12 predictors and a 1608 cases database. A pixel-based model was prepared and the scores zoned into 10 different types of slope units, obtained by differently combining two half-basin (HB) and four landform classification (LCL) coverages. The predictive performance of the 10 models were then compared to select the best performing one, whose prediction image was finally modified to consider also the propagation stage. The results attest integrating HB with LCL as more performing than using simple HB classification, with a very limited loss in predictive performance with respect to the pixel-based model
Bayesian multivariate linear regression · Data mining · Geomorphology · Landform · Landslide · Machine learning · Mars Exploration Program · Multivariate adaptive regression splines · Multivariate statistics · Pixel · Regression · Regression analysis · Statistics · Computer Science · Landslides and related hazards · Mathematics · Soil erosion and sediment transport · Tree Root and Stability Studies · Artificial Intelligence · Geology
| Citações por ano | 0,67 |
|---|---|
| Intervalo de citações | 2020 - 2023 (4) |
| Altamente citado | Não |