Reply on CC1 Article Swipe
Since the impoundment of the Three Gorges Reservoir area in 2003, the potential risks of geological disasters in the reservoir area have increased significantly, among which the hidden dangers of landslides are particularly prominent. To reduce casualties and damage, efficient and precise landslide susceptibility evaluation methods are important. Multiple ensemble models have been used to evaluate the susceptibility of the upper part of Badong County to landslides. In this study, EasyEnsemble technology was used to solve the imbalance between landslide and nonlandslide sample data. The extracted evaluation factors were input into three ensemble models, bagging, boosting, and stacking models, for training, and landslide susceptibility maps (LSMs) were drawn. According to the importance analysis, the important factors affecting the occurrence of landslides are altitude, terrain surface texture (TST), distance to residents, distance to rivers and land use. Comparing the influences of different grid sizes on the susceptibility results, a larger grid was found to lead to the overfitting of the prediction results. Therefore, a 30 m grid was selected as the evaluation unit. The accuracy rate, area under the curve (AUC), recall rate, test set precision, and Kappa coefficient of the multigrained cascade forest (gcForest) model under the stacking method were 0.958, 0.991, 0.965, 0.946, and 0.91, respectively, which were significantly better than the values produced by the other two models.
Related Topics
Concepts
Metadata
- Type
- peer-review
- Language
- en
- Landing Page
- https://doi.org/10.5194/egusphere-2022-697-ac3
- OA Status
- gold
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4294326045
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4294326045Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/egusphere-2022-697-ac3Digital Object Identifier
- Title
-
Reply on CC1Work title
- Type
-
peer-reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-01Full publication date if available
- Authors
-
Xueling WuList of authors in order
- Landing page
-
https://doi.org/10.5194/egusphere-2022-697-ac3Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5194/egusphere-2022-697-ac3Direct OA link when available
- Concepts
-
Overfitting, Landslide, Environmental science, Soil science, Hydrology (agriculture), Cartography, Geology, Statistics, Computer science, Mathematics, Artificial intelligence, Geography, Geomorphology, Geotechnical engineering, Artificial neural networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4294326045 |
|---|---|
| doi | https://doi.org/10.5194/egusphere-2022-697-ac3 |
| ids.doi | https://doi.org/10.5194/egusphere-2022-697-ac3 |
| ids.openalex | https://openalex.org/W4294326045 |
| fwci | |
| type | peer-review |
| title | Reply on CC1 |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10535 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9980000257492065 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2308 |
| topics[0].subfield.display_name | Management, Monitoring, Policy and Law |
| topics[0].display_name | Landslides and related hazards |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C22019652 |
| concepts[0].level | 3 |
| concepts[0].score | 0.6662126779556274 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q331309 |
| concepts[0].display_name | Overfitting |
| concepts[1].id | https://openalex.org/C186295008 |
| concepts[1].level | 2 |
| concepts[1].score | 0.62273108959198 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q167903 |
| concepts[1].display_name | Landslide |
| concepts[2].id | https://openalex.org/C39432304 |
| concepts[2].level | 0 |
| concepts[2].score | 0.4783565402030945 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[2].display_name | Environmental science |
| concepts[3].id | https://openalex.org/C159390177 |
| concepts[3].level | 1 |
| concepts[3].score | 0.3865135610103607 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q9161265 |
| concepts[3].display_name | Soil science |
| concepts[4].id | https://openalex.org/C76886044 |
| concepts[4].level | 2 |
| concepts[4].score | 0.3550238013267517 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2883300 |
| concepts[4].display_name | Hydrology (agriculture) |
| concepts[5].id | https://openalex.org/C58640448 |
| concepts[5].level | 1 |
| concepts[5].score | 0.3543274998664856 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q42515 |
| concepts[5].display_name | Cartography |
| concepts[6].id | https://openalex.org/C127313418 |
| concepts[6].level | 0 |
| concepts[6].score | 0.35228776931762695 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[6].display_name | Geology |
| concepts[7].id | https://openalex.org/C105795698 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3271292448043823 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[7].display_name | Statistics |
| concepts[8].id | https://openalex.org/C41008148 |
| concepts[8].level | 0 |
| concepts[8].score | 0.3043345808982849 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[8].display_name | Computer science |
| concepts[9].id | https://openalex.org/C33923547 |
| concepts[9].level | 0 |
| concepts[9].score | 0.24518632888793945 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[9].display_name | Mathematics |
| concepts[10].id | https://openalex.org/C154945302 |
| concepts[10].level | 1 |
| concepts[10].score | 0.22208744287490845 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[10].display_name | Artificial intelligence |
| concepts[11].id | https://openalex.org/C205649164 |
| concepts[11].level | 0 |
| concepts[11].score | 0.19563251733779907 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[11].display_name | Geography |
| concepts[12].id | https://openalex.org/C114793014 |
| concepts[12].level | 1 |
| concepts[12].score | 0.17748719453811646 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q52109 |
| concepts[12].display_name | Geomorphology |
| concepts[13].id | https://openalex.org/C187320778 |
| concepts[13].level | 1 |
| concepts[13].score | 0.17188125848770142 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q1349130 |
| concepts[13].display_name | Geotechnical engineering |
| concepts[14].id | https://openalex.org/C50644808 |
| concepts[14].level | 2 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[14].display_name | Artificial neural network |
| keywords[0].id | https://openalex.org/keywords/overfitting |
| keywords[0].score | 0.6662126779556274 |
| keywords[0].display_name | Overfitting |
| keywords[1].id | https://openalex.org/keywords/landslide |
| keywords[1].score | 0.62273108959198 |
| keywords[1].display_name | Landslide |
| keywords[2].id | https://openalex.org/keywords/environmental-science |
| keywords[2].score | 0.4783565402030945 |
| keywords[2].display_name | Environmental science |
| keywords[3].id | https://openalex.org/keywords/soil-science |
| keywords[3].score | 0.3865135610103607 |
| keywords[3].display_name | Soil science |
| keywords[4].id | https://openalex.org/keywords/hydrology |
| keywords[4].score | 0.3550238013267517 |
| keywords[4].display_name | Hydrology (agriculture) |
| keywords[5].id | https://openalex.org/keywords/cartography |
| keywords[5].score | 0.3543274998664856 |
| keywords[5].display_name | Cartography |
| keywords[6].id | https://openalex.org/keywords/geology |
| keywords[6].score | 0.35228776931762695 |
| keywords[6].display_name | Geology |
| keywords[7].id | https://openalex.org/keywords/statistics |
| keywords[7].score | 0.3271292448043823 |
| keywords[7].display_name | Statistics |
| keywords[8].id | https://openalex.org/keywords/computer-science |
| keywords[8].score | 0.3043345808982849 |
| keywords[8].display_name | Computer science |
| keywords[9].id | https://openalex.org/keywords/mathematics |
| keywords[9].score | 0.24518632888793945 |
| keywords[9].display_name | Mathematics |
| keywords[10].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[10].score | 0.22208744287490845 |
| keywords[10].display_name | Artificial intelligence |
| keywords[11].id | https://openalex.org/keywords/geography |
| keywords[11].score | 0.19563251733779907 |
| keywords[11].display_name | Geography |
| keywords[12].id | https://openalex.org/keywords/geomorphology |
| keywords[12].score | 0.17748719453811646 |
| keywords[12].display_name | Geomorphology |
| keywords[13].id | https://openalex.org/keywords/geotechnical-engineering |
| keywords[13].score | 0.17188125848770142 |
| keywords[13].display_name | Geotechnical engineering |
| language | en |
| locations[0].id | doi:10.5194/egusphere-2022-697-ac3 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | peer-review |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.5194/egusphere-2022-697-ac3 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5102008239 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-5125-472X |
| authorships[0].author.display_name | Xueling Wu |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I3124059619 |
| authorships[0].affiliations[0].raw_affiliation_string | Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China |
| authorships[0].institutions[0].id | https://openalex.org/I3124059619 |
| authorships[0].institutions[0].ror | https://ror.org/04gcegc37 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I3124059619 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | China University of Geosciences |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Xueling Wu |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.5194/egusphere-2022-697-ac3 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2022-09-02T00:00:00 |
| display_name | Reply on CC1 |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10535 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9980000257492065 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2308 |
| primary_topic.subfield.display_name | Management, Monitoring, Policy and Law |
| primary_topic.display_name | Landslides and related hazards |
| related_works | https://openalex.org/W2899084033, https://openalex.org/W3127411478, https://openalex.org/W2075075128, https://openalex.org/W2117308416, https://openalex.org/W1971903373, https://openalex.org/W2018207908, https://openalex.org/W2014956389, https://openalex.org/W2971743477, https://openalex.org/W2524472179, https://openalex.org/W2167493718 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.5194/egusphere-2022-697-ac3 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | peer-review |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.5194/egusphere-2022-697-ac3 |
| primary_location.id | doi:10.5194/egusphere-2022-697-ac3 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | peer-review |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.5194/egusphere-2022-697-ac3 |
| publication_date | 2022-09-01 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W3016580276, https://openalex.org/W2049785240, https://openalex.org/W1853011630, https://openalex.org/W4210949798, https://openalex.org/W872839841, https://openalex.org/W3126831744, https://openalex.org/W2009922693, https://openalex.org/W1945772893, https://openalex.org/W3121911063, https://openalex.org/W2001855588, https://openalex.org/W2117350110, https://openalex.org/W2097819183, https://openalex.org/W2023203753, https://openalex.org/W2886793380, https://openalex.org/W2088730795, https://openalex.org/W3122290846, https://openalex.org/W2038705219, https://openalex.org/W2931380018, https://openalex.org/W3039888984, https://openalex.org/W3154456184, https://openalex.org/W2191761327, https://openalex.org/W3128768470, https://openalex.org/W310439352 |
| referenced_works_count | 23 |
| abstract_inverted_index.a | 147, 162 |
| abstract_inverted_index.m | 164 |
| abstract_inverted_index.30 | 163 |
| abstract_inverted_index.In | 67 |
| abstract_inverted_index.To | 34 |
| abstract_inverted_index.as | 168 |
| abstract_inverted_index.by | 215 |
| abstract_inverted_index.in | 9, 17 |
| abstract_inverted_index.of | 3, 14, 29, 58, 62, 119, 139, 157, 188 |
| abstract_inverted_index.on | 143 |
| abstract_inverted_index.to | 54, 65, 74, 109, 128, 131, 152, 154 |
| abstract_inverted_index.The | 84, 172 |
| abstract_inverted_index.and | 37, 40, 80, 96, 101, 133, 185, 204 |
| abstract_inverted_index.are | 31, 46, 121 |
| abstract_inverted_index.for | 99 |
| abstract_inverted_index.set | 183 |
| abstract_inverted_index.the | 1, 4, 11, 18, 26, 56, 59, 76, 110, 113, 117, 137, 144, 155, 158, 169, 177, 189, 196, 212, 216 |
| abstract_inverted_index.two | 218 |
| abstract_inverted_index.was | 72, 150, 166 |
| abstract_inverted_index.area | 8, 20, 175 |
| abstract_inverted_index.been | 52 |
| abstract_inverted_index.grid | 141, 149, 165 |
| abstract_inverted_index.have | 21, 51 |
| abstract_inverted_index.into | 90 |
| abstract_inverted_index.land | 134 |
| abstract_inverted_index.lead | 153 |
| abstract_inverted_index.maps | 104 |
| abstract_inverted_index.part | 61 |
| abstract_inverted_index.test | 182 |
| abstract_inverted_index.than | 211 |
| abstract_inverted_index.this | 68 |
| abstract_inverted_index.use. | 135 |
| abstract_inverted_index.used | 53, 73 |
| abstract_inverted_index.were | 88, 106, 199, 208 |
| abstract_inverted_index.0.91, | 205 |
| abstract_inverted_index.2003, | 10 |
| abstract_inverted_index.Kappa | 186 |
| abstract_inverted_index.Since | 0 |
| abstract_inverted_index.Three | 5 |
| abstract_inverted_index.among | 24 |
| abstract_inverted_index.curve | 178 |
| abstract_inverted_index.data. | 83 |
| abstract_inverted_index.found | 151 |
| abstract_inverted_index.input | 89 |
| abstract_inverted_index.model | 194 |
| abstract_inverted_index.other | 217 |
| abstract_inverted_index.rate, | 174, 181 |
| abstract_inverted_index.risks | 13 |
| abstract_inverted_index.sizes | 142 |
| abstract_inverted_index.solve | 75 |
| abstract_inverted_index.three | 91 |
| abstract_inverted_index.under | 176, 195 |
| abstract_inverted_index.unit. | 171 |
| abstract_inverted_index.upper | 60 |
| abstract_inverted_index.which | 25, 207 |
| abstract_inverted_index.(AUC), | 179 |
| abstract_inverted_index.(LSMs) | 105 |
| abstract_inverted_index.(TST), | 126 |
| abstract_inverted_index.0.946, | 203 |
| abstract_inverted_index.0.958, | 200 |
| abstract_inverted_index.0.965, | 202 |
| abstract_inverted_index.0.991, | 201 |
| abstract_inverted_index.Badong | 63 |
| abstract_inverted_index.County | 64 |
| abstract_inverted_index.Gorges | 6 |
| abstract_inverted_index.better | 210 |
| abstract_inverted_index.drawn. | 107 |
| abstract_inverted_index.forest | 192 |
| abstract_inverted_index.hidden | 27 |
| abstract_inverted_index.larger | 148 |
| abstract_inverted_index.method | 198 |
| abstract_inverted_index.models | 50 |
| abstract_inverted_index.recall | 180 |
| abstract_inverted_index.reduce | 35 |
| abstract_inverted_index.rivers | 132 |
| abstract_inverted_index.sample | 82 |
| abstract_inverted_index.study, | 69 |
| abstract_inverted_index.values | 213 |
| abstract_inverted_index.between | 78 |
| abstract_inverted_index.cascade | 191 |
| abstract_inverted_index.damage, | 38 |
| abstract_inverted_index.dangers | 28 |
| abstract_inverted_index.factors | 87, 115 |
| abstract_inverted_index.methods | 45 |
| abstract_inverted_index.models, | 93, 98 |
| abstract_inverted_index.models. | 219 |
| abstract_inverted_index.precise | 41 |
| abstract_inverted_index.surface | 124 |
| abstract_inverted_index.terrain | 123 |
| abstract_inverted_index.texture | 125 |
| abstract_inverted_index.Multiple | 48 |
| abstract_inverted_index.accuracy | 173 |
| abstract_inverted_index.bagging, | 94 |
| abstract_inverted_index.distance | 127, 130 |
| abstract_inverted_index.ensemble | 49, 92 |
| abstract_inverted_index.evaluate | 55 |
| abstract_inverted_index.produced | 214 |
| abstract_inverted_index.results, | 146 |
| abstract_inverted_index.results. | 160 |
| abstract_inverted_index.selected | 167 |
| abstract_inverted_index.stacking | 97, 197 |
| abstract_inverted_index.According | 108 |
| abstract_inverted_index.Comparing | 136 |
| abstract_inverted_index.Reservoir | 7 |
| abstract_inverted_index.affecting | 116 |
| abstract_inverted_index.altitude, | 122 |
| abstract_inverted_index.analysis, | 112 |
| abstract_inverted_index.boosting, | 95 |
| abstract_inverted_index.different | 140 |
| abstract_inverted_index.disasters | 16 |
| abstract_inverted_index.efficient | 39 |
| abstract_inverted_index.extracted | 85 |
| abstract_inverted_index.imbalance | 77 |
| abstract_inverted_index.important | 114 |
| abstract_inverted_index.increased | 22 |
| abstract_inverted_index.landslide | 42, 79, 102 |
| abstract_inverted_index.potential | 12 |
| abstract_inverted_index.reservoir | 19 |
| abstract_inverted_index.training, | 100 |
| abstract_inverted_index.(gcForest) | 193 |
| abstract_inverted_index.Therefore, | 161 |
| abstract_inverted_index.casualties | 36 |
| abstract_inverted_index.evaluation | 44, 86, 170 |
| abstract_inverted_index.geological | 15 |
| abstract_inverted_index.importance | 111 |
| abstract_inverted_index.important. | 47 |
| abstract_inverted_index.influences | 138 |
| abstract_inverted_index.landslides | 30, 120 |
| abstract_inverted_index.occurrence | 118 |
| abstract_inverted_index.precision, | 184 |
| abstract_inverted_index.prediction | 159 |
| abstract_inverted_index.prominent. | 33 |
| abstract_inverted_index.residents, | 129 |
| abstract_inverted_index.technology | 71 |
| abstract_inverted_index.coefficient | 187 |
| abstract_inverted_index.impoundment | 2 |
| abstract_inverted_index.landslides. | 66 |
| abstract_inverted_index.overfitting | 156 |
| abstract_inverted_index.EasyEnsemble | 70 |
| abstract_inverted_index.multigrained | 190 |
| abstract_inverted_index.nonlandslide | 81 |
| abstract_inverted_index.particularly | 32 |
| abstract_inverted_index.respectively, | 206 |
| abstract_inverted_index.significantly | 209 |
| abstract_inverted_index.significantly, | 23 |
| abstract_inverted_index.susceptibility | 43, 57, 103, 145 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5102008239 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I3124059619 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.6899999976158142 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile |