Articles | Volume 14, issue 8
https://doi.org/10.5194/nhess-14-2215-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-14-2215-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
27 Aug 2014
Research article |
| 27 Aug 2014
Non-susceptible landslide areas in Italy and in the Mediterranean region
I. Marchesini
Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, 06128 Perugia, Italy
F. Ardizzone
Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, 06128 Perugia, Italy
M. Alvioli
Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, 06128 Perugia, Italy
Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, 06128 Perugia, Italy
Università degli Studi di Perugia, Dipartimento di Scienze della Terra, Piazza Università, 1, 06123 Perugia, Italy
F. Guzzetti
Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, 06128 Perugia, Italy
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Cited
39 citations as recorded by crossref.
- Measures of Spatial Autocorrelation Changes in Multitemporal SAR Images for Event Landslides Detection A. Mondini https://doi.org/10.3390/rs9060554
- Landslide susceptibility mapping using ensemble machine learning methods: a case study in Lombardy, Northern Italy Q. Xu et al. https://doi.org/10.1080/17538947.2024.2346263
- Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling M. Alvioli et al. https://doi.org/10.5194/gmd-9-3975-2016
- Correlación de variables morfométricas para deslizamientos en la cuenca del río Combeima, Colombia G. Santa-Ramírez et al. https://doi.org/10.17230/ingciencia.16.31.7
- Suitability assessment of global, continental and national digital elevation models for geomorphological analyses in Italy M. Zingaro et al. https://doi.org/10.1111/tgis.12845
- Parameter-free delineation of slope units and terrain subdivision of Italy M. Alvioli et al. https://doi.org/10.1016/j.geomorph.2020.107124
- Assessing and Improving Flood and Landslide Community Social Awareness and Engagement via a Web Platform: The Case of Italy D. Bignami et al. https://doi.org/10.1007/s13753-018-0199-0
- Earthquake-induced landslides susceptibility evaluation: A case study from the Abruzzo region (Central Italy) C. Carabella et al. https://doi.org/10.1016/j.catena.2021.105729
- Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory M. Loche et al. https://doi.org/10.1016/j.earscirev.2022.104125
- A review of statistically-based landslide susceptibility models P. Reichenbach et al. https://doi.org/10.1016/j.earscirev.2018.03.001
- Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai https://doi.org/10.1016/j.enggeo.2018.07.019
- Gender, age and circumstances analysis of flood and landslide fatalities in Italy P. Salvati et al. https://doi.org/10.1016/j.scitotenv.2017.08.064
- Space-time landslide predictive modelling L. Lombardo et al. https://doi.org/10.1016/j.earscirev.2020.103318
- Short to long term space-time prediction of rain-induced landslides under uncertainty A. Mondini et al. https://doi.org/10.1016/j.scitotenv.2025.179453
- Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China L. Yu et al. https://doi.org/10.1016/j.gsf.2024.101802
- A critical review on rainfall induced landslide occurrences and early warning systems K. Ghosh & R. Saha https://doi.org/10.1080/17486025.2025.2496302
- Window-Based Morphometric Indices as Predictive Variables for Landslide Susceptibility Models N. Barbosa et al. https://doi.org/10.3390/rs13030451
- Overcoming the data limitations in landslide susceptibility modeling J. Woodard & B. Mirus https://doi.org/10.1126/sciadv.adt1541
- A spaceborne SAR-based procedure to support the detection of landslides G. Esposito et al. https://doi.org/10.5194/nhess-20-2379-2020
- Impacts of past and future land changes on landslides in southern Italy S. Gariano et al. https://doi.org/10.1007/s10113-017-1210-9
- A global landslide non-susceptibility map G. Jia et al. https://doi.org/10.1016/j.geomorph.2021.107804
- Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran A. Arabameri et al. https://doi.org/10.3390/rs12030475
- Synthetic libraries of urban landslide simulations to identify slope failure hotspots and drivers across spatial scales and landscapes E. Bozzolan et al. https://doi.org/10.1007/s10346-024-02327-4
- Automatic mapping of event landslides at basin scale in Taiwan using a Montecarlo approach and synthetic land cover fingerprints A. Mondini et al. https://doi.org/10.1016/j.jag.2017.07.016
- Topography-driven satellite imagery analysis for landslide mapping M. Alvioli et al. https://doi.org/10.1080/19475705.2018.1458050
- SANF-RFI: An operational early warning system for rainfall-induced landslides along the Italian railway network E. Murgia et al. https://doi.org/10.1177/09544097261456885
- Geoenvironmental conditioning of landsliding in river valleys of lowland regions and its significance in landslide susceptibility assessment: A case study in the Lower Vistula Valley, Northern Poland D. Grabowski et al. https://doi.org/10.1016/j.geomorph.2022.108490
- Landslide mobilization rates: A global analysis and model J. Broeckx et al. https://doi.org/10.1016/j.earscirev.2019.102972
- A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods M. Sinčić et al. https://doi.org/10.3390/rs16162923
- Natural Hazard Risk Assessments Applicable to Enterprise Risk Management J. Keaton & R. Munro https://doi.org/10.21663/EEG-D-24-00038
- The 10 m-resolution TINITALY DEM as a trans-disciplinary basis for the analysis of the Italian territory: Current trends and new perspectives S. Tarquini & L. Nannipieri https://doi.org/10.1016/j.geomorph.2016.12.022
- Inventory of Historical and Recent Earthquake-Triggered Landslides and Assessment of Related Susceptibility by GIS-Based Analytic Hierarchy Process: The Case of Cephalonia (Ionian Islands, Western Greece) S. Mavroulis et al. https://doi.org/10.3390/app12062895
- GIS-Based Spatiotemporal Trend Analysis of Landslide Occurrence in Türkiye: A Comparative Assessment of NonParametric Methods Using a National Inventory (1950–2023) H. Yurtseven et al. https://doi.org/10.1007/s13369-026-11742-9
- Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches B. Pham et al. https://doi.org/10.1016/j.catena.2018.12.018
- Evaluating the Response of Hydrological Stress Indices Using the CHyM Model over a Wide Area in Central Italy A. Lombardi et al. https://doi.org/10.3390/hydrology9080139
- Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model S. Ma et al. https://doi.org/10.3390/f15040678
- Geographical landslide early warning systems F. Guzzetti et al. https://doi.org/10.1016/j.earscirev.2019.102973
- Deep learning forecast of rainfall-induced shallow landslides A. Mondini et al. https://doi.org/10.1038/s41467-023-38135-y
- National-scale assessment of railways exposure to rapid flow-like landslides I. Marchesini et al. https://doi.org/10.1016/j.enggeo.2024.107474
39 citations as recorded by crossref.
- Measures of Spatial Autocorrelation Changes in Multitemporal SAR Images for Event Landslides Detection A. Mondini https://doi.org/10.3390/rs9060554
- Landslide susceptibility mapping using ensemble machine learning methods: a case study in Lombardy, Northern Italy Q. Xu et al. https://doi.org/10.1080/17538947.2024.2346263
- Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling M. Alvioli et al. https://doi.org/10.5194/gmd-9-3975-2016
- Correlación de variables morfométricas para deslizamientos en la cuenca del río Combeima, Colombia G. Santa-Ramírez et al. https://doi.org/10.17230/ingciencia.16.31.7
- Suitability assessment of global, continental and national digital elevation models for geomorphological analyses in Italy M. Zingaro et al. https://doi.org/10.1111/tgis.12845
- Parameter-free delineation of slope units and terrain subdivision of Italy M. Alvioli et al. https://doi.org/10.1016/j.geomorph.2020.107124
- Assessing and Improving Flood and Landslide Community Social Awareness and Engagement via a Web Platform: The Case of Italy D. Bignami et al. https://doi.org/10.1007/s13753-018-0199-0
- Earthquake-induced landslides susceptibility evaluation: A case study from the Abruzzo region (Central Italy) C. Carabella et al. https://doi.org/10.1016/j.catena.2021.105729
- Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory M. Loche et al. https://doi.org/10.1016/j.earscirev.2022.104125
- A review of statistically-based landslide susceptibility models P. Reichenbach et al. https://doi.org/10.1016/j.earscirev.2018.03.001
- Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai https://doi.org/10.1016/j.enggeo.2018.07.019
- Gender, age and circumstances analysis of flood and landslide fatalities in Italy P. Salvati et al. https://doi.org/10.1016/j.scitotenv.2017.08.064
- Space-time landslide predictive modelling L. Lombardo et al. https://doi.org/10.1016/j.earscirev.2020.103318
- Short to long term space-time prediction of rain-induced landslides under uncertainty A. Mondini et al. https://doi.org/10.1016/j.scitotenv.2025.179453
- Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China L. Yu et al. https://doi.org/10.1016/j.gsf.2024.101802
- A critical review on rainfall induced landslide occurrences and early warning systems K. Ghosh & R. Saha https://doi.org/10.1080/17486025.2025.2496302
- Window-Based Morphometric Indices as Predictive Variables for Landslide Susceptibility Models N. Barbosa et al. https://doi.org/10.3390/rs13030451
- Overcoming the data limitations in landslide susceptibility modeling J. Woodard & B. Mirus https://doi.org/10.1126/sciadv.adt1541
- A spaceborne SAR-based procedure to support the detection of landslides G. Esposito et al. https://doi.org/10.5194/nhess-20-2379-2020
- Impacts of past and future land changes on landslides in southern Italy S. Gariano et al. https://doi.org/10.1007/s10113-017-1210-9
- A global landslide non-susceptibility map G. Jia et al. https://doi.org/10.1016/j.geomorph.2021.107804
- Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran A. Arabameri et al. https://doi.org/10.3390/rs12030475
- Synthetic libraries of urban landslide simulations to identify slope failure hotspots and drivers across spatial scales and landscapes E. Bozzolan et al. https://doi.org/10.1007/s10346-024-02327-4
- Automatic mapping of event landslides at basin scale in Taiwan using a Montecarlo approach and synthetic land cover fingerprints A. Mondini et al. https://doi.org/10.1016/j.jag.2017.07.016
- Topography-driven satellite imagery analysis for landslide mapping M. Alvioli et al. https://doi.org/10.1080/19475705.2018.1458050
- SANF-RFI: An operational early warning system for rainfall-induced landslides along the Italian railway network E. Murgia et al. https://doi.org/10.1177/09544097261456885
- Geoenvironmental conditioning of landsliding in river valleys of lowland regions and its significance in landslide susceptibility assessment: A case study in the Lower Vistula Valley, Northern Poland D. Grabowski et al. https://doi.org/10.1016/j.geomorph.2022.108490
- Landslide mobilization rates: A global analysis and model J. Broeckx et al. https://doi.org/10.1016/j.earscirev.2019.102972
- A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods M. Sinčić et al. https://doi.org/10.3390/rs16162923
- Natural Hazard Risk Assessments Applicable to Enterprise Risk Management J. Keaton & R. Munro https://doi.org/10.21663/EEG-D-24-00038
- The 10 m-resolution TINITALY DEM as a trans-disciplinary basis for the analysis of the Italian territory: Current trends and new perspectives S. Tarquini & L. Nannipieri https://doi.org/10.1016/j.geomorph.2016.12.022
- Inventory of Historical and Recent Earthquake-Triggered Landslides and Assessment of Related Susceptibility by GIS-Based Analytic Hierarchy Process: The Case of Cephalonia (Ionian Islands, Western Greece) S. Mavroulis et al. https://doi.org/10.3390/app12062895
- GIS-Based Spatiotemporal Trend Analysis of Landslide Occurrence in Türkiye: A Comparative Assessment of NonParametric Methods Using a National Inventory (1950–2023) H. Yurtseven et al. https://doi.org/10.1007/s13369-026-11742-9
- Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches B. Pham et al. https://doi.org/10.1016/j.catena.2018.12.018
- Evaluating the Response of Hydrological Stress Indices Using the CHyM Model over a Wide Area in Central Italy A. Lombardi et al. https://doi.org/10.3390/hydrology9080139
- Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model S. Ma et al. https://doi.org/10.3390/f15040678
- Geographical landslide early warning systems F. Guzzetti et al. https://doi.org/10.1016/j.earscirev.2019.102973
- Deep learning forecast of rainfall-induced shallow landslides A. Mondini et al. https://doi.org/10.1038/s41467-023-38135-y
- National-scale assessment of railways exposure to rapid flow-like landslides I. Marchesini et al. https://doi.org/10.1016/j.enggeo.2024.107474
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