Articles | Volume 22, issue 4
https://doi.org/10.5194/nhess-22-1395-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-1395-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Apr 2022
Research article |
| 21 Apr 2022
| 21 Apr 2022
Assessing the importance of conditioning factor selection in landslide susceptibility for the province of Belluno (region of Veneto, northeastern Italy)
Sansar Raj Meena
CORRESPONDING AUTHOR
Department of Geosciences, University of Padua, Padua, Italy
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
Silvia Puliero
Department of Geosciences, University of Padua, Padua, Italy
Kushanav Bhuyan
Department of Geosciences, University of Padua, Padua, Italy
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
Mario Floris
Department of Geosciences, University of Padua, Padua, Italy
Filippo Catani
Department of Geosciences, University of Padua, Padua, Italy
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Cited
15 citations as recorded by crossref.
- Optimization of negative sample selection for landslide susceptibility mapping based on machine learning using K-means-KNN algorithm C. Liu 10.1007/s12145-023-01151-z
- Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam H. Nguyen et al. 10.1002/gj.4885
- Mitigation measures preventing floods from landslide dams: analysis of pre- and post-hydrologic conditions upstream a seismic-induced landslide dam in Central Italy C. Cencetti & L. Di Matteo 10.1007/s12665-022-10515-5
- The Prediction of Cross-Regional Landslide Susceptibility Based on Pixel Transfer Learning X. Wang et al. 10.3390/rs16020347
- Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique C. Zhou et al. 10.1080/10106049.2024.2327463
- Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling A. Dahal & L. Lombardo 10.1016/j.cageo.2023.105364
- Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia’s mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis S. Alqadhi et al. 10.1007/s11356-023-31352-4
- High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data N. Sharma et al. 10.1016/j.catena.2023.107653
- The suitability of different vegetation indices to analyses area with landslide propensity using Sentinel -2 Image L. Giordano et al. 10.1590/s1982-21702023000300008
- Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods E. Bravo-López et al. 10.3390/land12061135
- Modeling windthrow effects on water runoff and hillslope stability in a mountain catchment affected by the VAIA storm L. Mauri & P. Tarolli 10.1016/j.scitotenv.2023.164831
- Machine-Learning-Based Hybrid Modeling for Geological Hazard Susceptibility Assessment in Wudou District, Bailong River Basin, China Z. Wang et al. 10.3390/geohazards4020010
- Geospatial assessment of landslide-prone areas in the southern part of Anambra State, Nigeria using classical statistical models V. Nwazelibe & J. Egbueri 10.1007/s12665-024-11533-1
- Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data K. Bhuyan et al. 10.1038/s41598-022-27352-y
- Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes) M. Herrera-Coy et al. 10.3390/rs15153870
15 citations as recorded by crossref.
- Optimization of negative sample selection for landslide susceptibility mapping based on machine learning using K-means-KNN algorithm C. Liu 10.1007/s12145-023-01151-z
- Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam H. Nguyen et al. 10.1002/gj.4885
- Mitigation measures preventing floods from landslide dams: analysis of pre- and post-hydrologic conditions upstream a seismic-induced landslide dam in Central Italy C. Cencetti & L. Di Matteo 10.1007/s12665-022-10515-5
- The Prediction of Cross-Regional Landslide Susceptibility Based on Pixel Transfer Learning X. Wang et al. 10.3390/rs16020347
- Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique C. Zhou et al. 10.1080/10106049.2024.2327463
- Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling A. Dahal & L. Lombardo 10.1016/j.cageo.2023.105364
- Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia’s mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis S. Alqadhi et al. 10.1007/s11356-023-31352-4
- High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data N. Sharma et al. 10.1016/j.catena.2023.107653
- The suitability of different vegetation indices to analyses area with landslide propensity using Sentinel -2 Image L. Giordano et al. 10.1590/s1982-21702023000300008
- Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods E. Bravo-López et al. 10.3390/land12061135
- Modeling windthrow effects on water runoff and hillslope stability in a mountain catchment affected by the VAIA storm L. Mauri & P. Tarolli 10.1016/j.scitotenv.2023.164831
- Machine-Learning-Based Hybrid Modeling for Geological Hazard Susceptibility Assessment in Wudou District, Bailong River Basin, China Z. Wang et al. 10.3390/geohazards4020010
- Geospatial assessment of landslide-prone areas in the southern part of Anambra State, Nigeria using classical statistical models V. Nwazelibe & J. Egbueri 10.1007/s12665-024-11533-1
- Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data K. Bhuyan et al. 10.1038/s41598-022-27352-y
- Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes) M. Herrera-Coy et al. 10.3390/rs15153870
Latest update: 24 Apr 2024
Short summary
The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and machine learning algorithms.
The study investigated the importance of the conditioning factors in predicting landslide...
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