Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models
The aim of this study is to identify the landslide predisposing factors' combination using a bivariate statistical model that best predicts landslide susceptibility. The best model is one that has simultaneously good performance in terms of suitability and predictive power and has been develope...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2012-04-01
|
| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | http://www.nat-hazards-earth-syst-sci.net/12/979/2012/nhess-12-979-2012.pdf |
| _version_ | 1811191671195435008 |
|---|---|
| author | S. Pereira J. L. Zêzere C. Bateira |
| author_facet | S. Pereira J. L. Zêzere C. Bateira |
| author_sort | S. Pereira |
| collection | DOAJ |
| description | The aim of this study is to identify the landslide predisposing factors' combination using a bivariate statistical model that best predicts landslide susceptibility. The best model is one that has simultaneously good performance in terms of suitability and predictive power and has been developed using variables that are conditionally independent. The study area is the Santa Marta de Penaguião council (70 km<sup>2</sup>) located in the Northern Portugal. <br><br> In order to identify the best combination of landslide predisposing factors, all possible combinations using up to seven predisposing factors were performed, which resulted in 120 predictions that were assessed with a landside inventory containing 767 shallow translational slides. The best landslide susceptibility model was selected according to the model degree of fitness and on the basis of a conditional independence criterion. The best model was developed with only three landslide predisposing factors (slope angle, inverse wetness index, and land use) and was compared with a model developed using all seven landslide predisposing factors. <br><br> Results showed that it is possible to produce a reliable landslide susceptibility model using fewer landslide predisposing factors, which contributes towards higher conditional independence. |
| first_indexed | 2024-04-11T23:39:41Z |
| format | Article |
| id | doaj.art-04df78da34694e5ab471b70c9344cda8 |
| institution | Directory Open Access Journal |
| issn | 1561-8633 1684-9981 |
| language | English |
| last_indexed | 2024-04-11T23:39:41Z |
| publishDate | 2012-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj.art-04df78da34694e5ab471b70c9344cda82022-12-22T03:56:49ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812012-04-0112497998810.5194/nhess-12-979-2012Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility modelsS. PereiraJ. L. ZêzereC. BateiraThe aim of this study is to identify the landslide predisposing factors' combination using a bivariate statistical model that best predicts landslide susceptibility. The best model is one that has simultaneously good performance in terms of suitability and predictive power and has been developed using variables that are conditionally independent. The study area is the Santa Marta de Penaguião council (70 km<sup>2</sup>) located in the Northern Portugal. <br><br> In order to identify the best combination of landslide predisposing factors, all possible combinations using up to seven predisposing factors were performed, which resulted in 120 predictions that were assessed with a landside inventory containing 767 shallow translational slides. The best landslide susceptibility model was selected according to the model degree of fitness and on the basis of a conditional independence criterion. The best model was developed with only three landslide predisposing factors (slope angle, inverse wetness index, and land use) and was compared with a model developed using all seven landslide predisposing factors. <br><br> Results showed that it is possible to produce a reliable landslide susceptibility model using fewer landslide predisposing factors, which contributes towards higher conditional independence.http://www.nat-hazards-earth-syst-sci.net/12/979/2012/nhess-12-979-2012.pdf |
| spellingShingle | S. Pereira J. L. Zêzere C. Bateira Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models Natural Hazards and Earth System Sciences |
| title | Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models |
| title_full | Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models |
| title_fullStr | Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models |
| title_full_unstemmed | Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models |
| title_short | Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models |
| title_sort | technical note assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models |
| url | http://www.nat-hazards-earth-syst-sci.net/12/979/2012/nhess-12-979-2012.pdf |
| work_keys_str_mv | AT spereira technicalnoteassessingpredictivecapacityandconditionalindependenceoflandslidepredisposingfactorsforshallowlandslidesusceptibilitymodels AT jlzezere technicalnoteassessingpredictivecapacityandconditionalindependenceoflandslidepredisposingfactorsforshallowlandslidesusceptibilitymodels AT cbateira technicalnoteassessingpredictivecapacityandconditionalindependenceoflandslidepredisposingfactorsforshallowlandslidesusceptibilitymodels |