Articles | Volume 9, issue 10
https://doi.org/10.5194/gmd-9-3533-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-3533-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0
CNR IRPI, via Madonna Alta 126, 06128 Perugia, Italy
Paola Reichenbach
CNR IRPI, via Madonna Alta 126, 06128 Perugia, Italy
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59 citations as recorded by crossref.
- Capturing the footprints of ground motion in the spatial distribution of rainfall-induced landslides H. Tanyaş et al. 10.1007/s10064-021-02238-x
- Rainfall-Induced Landslide Assessment under Different Precipitation Thresholds Using Remote Sensing Data: A Central Andes Case G. Maragaño-Carmona et al. 10.3390/w15142514
- Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms M. Amiri et al. 10.1016/j.geoderma.2018.12.042
- Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping Y. Wu et al. 10.1016/j.catena.2019.104396
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- Effective surveyed area and its role in statistical landslide susceptibility assessments T. Bornaetxea et al. 10.5194/nhess-18-2455-2018
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- Effects of Landslide Sampling Strategies on the Prediction Skill of Landslide Susceptibility Modelings S. Tekin & T. Çan 10.1007/s12524-018-0800-4
- GIS-based analysis of landslides susceptibility mapping: a case study of Lushoto district, north-eastern Tanzania M. Makonyo & Z. Zahor 10.1007/s11069-023-06038-2
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- A global slope unit-based method for the near real-time prediction of earthquake-induced landslides H. Tanyas et al. 10.1016/j.geomorph.2018.10.022
- Using machine learning algorithms to map the groundwater recharge potential zones H. Pourghasemi et al. 10.1016/j.jenvman.2020.110525
- Economic landslide susceptibility under a socio-economic perspective: an application to Umbria Region (Central Italy) M. Donnini et al. 10.1007/s10037-020-00143-6
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- A spaceborne SAR-based procedure to support the detection of landslides G. Esposito et al. 10.5194/nhess-20-2379-2020
- Landslide Susceptibility Assessment Tools v1.0.0b – Project Manager Suite: a new modular toolkit for landslide susceptibility assessment J. Torizin et al. 10.5194/gmd-15-2791-2022
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- Landslide susceptibility assessment on a large scale in the Podsljeme area, City of Zagreb (Croatia) S. Bernat Gazibara et al. 10.1080/17445647.2022.2163197
- Landslide Susceptibility Mapping in Terms of the Slope-Unit or Raster-Unit, Which is Better? S. Ma et al. 10.1007/s12583-021-1407-1
- Aplicação do índice estatístico e análise multicritério no mapeamento da suscetibilidade a deslizamentos, no município do Ipojuca, Pernambuco, Brasil C. Duarte et al. 10.26848/rbgf.v17.2.p1015-1037
- Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models R. Schlögel et al. 10.1016/j.geomorph.2017.10.018
- LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation M. Rossi et al. 10.5194/gmd-15-5651-2022
- Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster L. Lombardo et al. 10.1007/s00477-018-1518-0
- Mapping Susceptibility With Open-Source Tools: A New Plugin for QGIS G. Titti et al. 10.3389/feart.2022.842425
- Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management? H. Pourghasemi et al. 10.1016/j.gsf.2019.10.008
- Assessment of land subsidence susceptibility in Semnan plain (Iran): a comparison of support vector machine and weights of evidence data mining algorithms M. Mohammady et al. 10.1007/s11069-019-03785-z
- Automatic delineation of geomorphological slope units with <tt>r.slopeunits v1.0</tt> and their optimization for landslide susceptibility modeling M. Alvioli et al. 10.5194/gmd-9-3975-2016
- Characteristics and causes of natural and human-induced landslides in a tropical mountainous region: the rift flank west of Lake Kivu (Democratic Republic of the Congo) J. Maki Mateso et al. 10.5194/nhess-23-643-2023
- Potential suitability mapping evaluation for ecotourism development in Darjeeling Himalayan region of India D. Roy et al. 10.1080/14724049.2023.2272059
- Implementing landslide path dependency in landslide susceptibility modelling J. Samia et al. 10.1007/s10346-018-1024-y
- Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh M. Rahman et al. 10.1016/j.jenvman.2021.113086
- Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods Q. He et al. 10.3390/rs13081572
- GIS‐based susceptibility assessment of the occurrence of gully headcuts and pipe collapses in a semi‐arid environment: Golestan Province, NE Iran N. Kariminejad et al. 10.1002/ldr.3397
- Improvement of landslide hazard assessments for regulatory zoning in France: STATE–OF–THE-ART perspectives and considerations Y. Thiery et al. 10.1016/j.ijdrr.2020.101562
- Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai 10.1016/j.enggeo.2018.07.019
- Landslide susceptibility mapping in East Ungaran, Indonesia: A comparative study using statistical methods D. Maulana et al. 10.15243/jdmlm.2024.114.6107
- Reliability of water content estimation by profile probe and its effect on slope stability L. Di Matteo et al. 10.1007/s10346-017-0895-7
- A review of statistically-based landslide susceptibility models P. Reichenbach et al. 10.1016/j.earscirev.2018.03.001
1 citations as recorded by crossref.
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Short summary
Landslide susceptibility maps show places where landslides may occur in the future. These maps are prepared using different approaches, information on past landslides distribution and a variety of geo-environmental data. The paper describes LAND-SE (LANDslide Susceptibility Evaluation), an open-source software coded in R for statistically based susceptibility zonation that provides estimates of model performances and uncertainty. A user guide and example data are distributed with the software.
Landslide susceptibility maps show places where landslides may occur in the future. These maps...