Evaluating Shallow Landslide Prediction Mapping by Using Two Different GIS-Based Models
4SLIDE and SHALSTAB
Bibliographic Data
| ID | 22032666 |
|---|---|
| Authors | Federico Valerio Moresi (0000-0003-4648-4373, Università degli Studi della Tuscia, corresponding author), Mauro Maesano (0000-0002-4325-951X, Università degli Studi della Tuscia), Marco di Cristofaro (0009-0004-1451-7219, Università degli Studi della Tuscia), Giuseppe Scarascia Mugnozza (0000-0003-0357-4360, European Forest Institute, Biocities, 00189 Roma, Italy), Elena Brunori (0000-0002-1475-1353, Università degli Studi della Tuscia) |
| Year | 2025 |
| Volume | 14 |
| Issue | 4 |
| Pages | 144 |
| Publication date | 2025-03-27 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14040144 |
| OpenAlex | W4408903381 |
| Language | EN |
| Citations received | 2 |
| References cited | 34 |
Landslides affecting soil layers up to 1–2 m deep pose a significant hazard in mountainous and hilly regions, particularly in the Mediterranean, where intense precipitation is increasing. Identifying landslide-prone areas is crucial for risk assessment and mitigation, as landslides can severely impact land surfaces, infrastructure, and inhabited areas. Forest cover and management play a fundamental role in stabilizing soil and reducing landslide susceptibility. This study focuses on landslide forecasting models, which integrate geological, climatic, and topographic data to predict landslide probability and severity. Specifically, we compare the predictive accuracy of the 4SLIDE model with the established SHALSTAB model in a forested mountain catchment within Sila National Park, Southern Italy, using GIS-based analysis. The results demonstrate that both models effectively identify high-risk areas, with ROC analysis confirming the superior predictive capability of the 4SLIDE model. Our findings underscore the critical importance of early landslide identification, supporting timely interventions and the implementation of forest engineering and Civil Protection measures to mitigate the impact of landslides on communities and infrastructure
Cartography · Geography · Geotechnical engineering · Landslide · Computer Science · Geotechnical Engineering and Analysis · Landslides and related hazards · Soil and Unsaturated Flow · Geology
| Citations per year | 2 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |