Plots showing Mean Decrease in Accuracy of the three landslide susceptibility models developed using the RF algorithm for the (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods.
<p>The plots show the relative importance of different variables in predicting landslide susceptibility in each period.</p>
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2024
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| _version_ | 1852025664914325504 |
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| author | Raja Das (1841107) |
| author2 | Pham Van Tien (19949682) Karl W. Wegmann (19949685) Madhumita Chakraborty (485342) |
| author2_role | author author author |
| author_facet | Raja Das (1841107) Pham Van Tien (19949682) Karl W. Wegmann (19949685) Madhumita Chakraborty (485342) |
| author_role | author |
| dc.creator.none.fl_str_mv | Raja Das (1841107) Pham Van Tien (19949682) Karl W. Wegmann (19949685) Madhumita Chakraborty (485342) |
| dc.date.none.fl_str_mv | 2024-10-25T17:24:00Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0308494.g005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Plots_showing_Mean_Decrease_in_Accuracy_of_the_three_landslide_susceptibility_models_developed_using_the_RF_algorithm_for_the_a_post-Ketsana_b_post-Podul_and_c_post-Molave_periods_/27305280 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neuroscience Sociology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified notable areal expansion nation &# 8217 annual average rainfall 90 %) scores risk reduction strategies elevated landslide susceptibility consistently high susceptibility sensitivity (& gt molave period compared regional susceptibility trends temporal landslide inventories high landslide susceptibility increasing landslide susceptibility landslide susceptibility (& gt typhoon molave moderate reduction ketsana period landslide probability temporal variations temporal analysis vietnam pose typhoon ketsana topographic elevation spatially mapped southern areas several years scale variation results indicate research found research explores research examines prone regions probabilistic occurrences prioritizing mitigation nonlinear relationship many parts machine learning longstanding threat identifying areas hydroclimatic conditions growing intensity eastern regions early indicators climatic conditions changing hydro based assessment 89 %), 2020 ). 2013 ), |
| dc.title.none.fl_str_mv | Plots showing Mean Decrease in Accuracy of the three landslide susceptibility models developed using the RF algorithm for the (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The plots show the relative importance of different variables in predicting landslide susceptibility in each period.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_f979b92e8df528a6a3d29ef2ba65c5f3 |
| identifier_str_mv | 10.1371/journal.pone.0308494.g005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27305280 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Plots showing Mean Decrease in Accuracy of the three landslide susceptibility models developed using the RF algorithm for the (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods.Raja Das (1841107)Pham Van Tien (19949682)Karl W. Wegmann (19949685)Madhumita Chakraborty (485342)NeuroscienceSociologyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiednotable areal expansionnation &# 8217annual average rainfall90 %) scoresrisk reduction strategieselevated landslide susceptibilityconsistently high susceptibilitysensitivity (& gtmolave period comparedregional susceptibility trendstemporal landslide inventorieshigh landslide susceptibilityincreasing landslide susceptibilitylandslide susceptibility(& gttyphoon molavemoderate reductionketsana periodlandslide probabilitytemporal variationstemporal analysisvietnam posetyphoon ketsanatopographic elevationspatially mappedsouthern areasseveral yearsscale variationresults indicateresearch foundresearch exploresresearch examinesprone regionsprobabilistic occurrencesprioritizing mitigationnonlinear relationshipmany partsmachine learninglongstanding threatidentifying areashydroclimatic conditionsgrowing intensityeastern regionsearly indicatorsclimatic conditionschanging hydrobased assessment89 %),2020 ).2013 ),<p>The plots show the relative importance of different variables in predicting landslide susceptibility in each period.</p>2024-10-25T17:24:00ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0308494.g005https://figshare.com/articles/figure/Plots_showing_Mean_Decrease_in_Accuracy_of_the_three_landslide_susceptibility_models_developed_using_the_RF_algorithm_for_the_a_post-Ketsana_b_post-Podul_and_c_post-Molave_periods_/27305280CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/273052802024-10-25T17:24:00Z |
| spellingShingle | Plots showing Mean Decrease in Accuracy of the three landslide susceptibility models developed using the RF algorithm for the (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods. Raja Das (1841107) Neuroscience Sociology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified notable areal expansion nation &# 8217 annual average rainfall 90 %) scores risk reduction strategies elevated landslide susceptibility consistently high susceptibility sensitivity (& gt molave period compared regional susceptibility trends temporal landslide inventories high landslide susceptibility increasing landslide susceptibility landslide susceptibility (& gt typhoon molave moderate reduction ketsana period landslide probability temporal variations temporal analysis vietnam pose typhoon ketsana topographic elevation spatially mapped southern areas several years scale variation results indicate research found research explores research examines prone regions probabilistic occurrences prioritizing mitigation nonlinear relationship many parts machine learning longstanding threat identifying areas hydroclimatic conditions growing intensity eastern regions early indicators climatic conditions changing hydro based assessment 89 %), 2020 ). 2013 ), |
| status_str | publishedVersion |
| title | Plots showing Mean Decrease in Accuracy of the three landslide susceptibility models developed using the RF algorithm for the (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods. |
| title_full | Plots showing Mean Decrease in Accuracy of the three landslide susceptibility models developed using the RF algorithm for the (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods. |
| title_fullStr | Plots showing Mean Decrease in Accuracy of the three landslide susceptibility models developed using the RF algorithm for the (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods. |
| title_full_unstemmed | Plots showing Mean Decrease in Accuracy of the three landslide susceptibility models developed using the RF algorithm for the (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods. |
| title_short | Plots showing Mean Decrease in Accuracy of the three landslide susceptibility models developed using the RF algorithm for the (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods. |
| title_sort | Plots showing Mean Decrease in Accuracy of the three landslide susceptibility models developed using the RF algorithm for the (a) post-Ketsana, (b) post-Podul, and (c) post-Molave periods. |
| topic | Neuroscience Sociology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified notable areal expansion nation &# 8217 annual average rainfall 90 %) scores risk reduction strategies elevated landslide susceptibility consistently high susceptibility sensitivity (& gt molave period compared regional susceptibility trends temporal landslide inventories high landslide susceptibility increasing landslide susceptibility landslide susceptibility (& gt typhoon molave moderate reduction ketsana period landslide probability temporal variations temporal analysis vietnam pose typhoon ketsana topographic elevation spatially mapped southern areas several years scale variation results indicate research found research explores research examines prone regions probabilistic occurrences prioritizing mitigation nonlinear relationship many parts machine learning longstanding threat identifying areas hydroclimatic conditions growing intensity eastern regions early indicators climatic conditions changing hydro based assessment 89 %), 2020 ). 2013 ), |