Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility

Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency rati...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Remote sensing (Basel, Switzerland) Ročník 13; číslo 24; s. 4966
Hlavní autoři: Liu, Ru, Peng, Jianbing, Leng, Yanqiu, Lee, Saro, Panahi, Mahdi, Chen, Wei, Zhao, Xia
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.12.2021
Témata:
Algorithms
Artificial intelligence
Conditioning
Correlation analysis
Curvature
data collection
Datasets
Decision making
Disasters
Elevation
Fault lines
firefly algorithm
Geographic information systems
Geology
grey wolf optimizer algorithm
Heuristic methods
Human engineering
hybrid model
Hybrids
inventories
Land use planning
landslide susceptibility
Landslides
Landslides & mudslides
Lithology
Machine learning
Mapping
Natural disasters
Neural networks
Normalized difference vegetative index
Performance enhancement
prediction
Predictions
rain
Rainfall
regression analysis
Remote sensing
Sediment transport
Software
soil
Soil conditions
Spatial analysis
streams
Support vector machines
support vector regression algorithm
Topography
Training
Vegetation
ISSN:2072-4292, 2072-4292
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency ratio (FR) preprocessed. Therefore, a landslide inventory composed of 140 landslides and 16 landslide conditioning factors is compiled as a landslide database. Among these landslides, 70% (98) landslides were randomly selected as the training dataset of the model, and the other landslides (42) were used to verify the model. The 16 landslide conditioning factors include elevation, slope, aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), landslide, rainfall, soil and lithology. The conditioning factors selection and spatial correlation analysis were carried out by using the correlation attribute evaluation (CAE) method and the frequency ratio (FR) algorithm. The area under the receiver operating characteristic curve (AUROC) and kappa data of the training dataset and validation dataset are used to evaluate the prediction ability and the relationship between the advantages and disadvantages of landslide susceptibility maps. The results show that the SVR-GWO model (AUROC = 0.854) has the best performance in landslide spatial prediction, followed by the SVR-FA (AUROC = 0.838) and SVR models (AUROC = 0.818). The hybrid models of SVR-GWO and SVR-FA improve the performance of the single SVR model, and all three models have good prospects for regional-scale landslide spatial modeling.
AbstractList Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency ratio (FR) preprocessed. Therefore, a landslide inventory composed of 140 landslides and 16 landslide conditioning factors is compiled as a landslide database. Among these landslides, 70% (98) landslides were randomly selected as the training dataset of the model, and the other landslides (42) were used to verify the model. The 16 landslide conditioning factors include elevation, slope, aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, sediment transport index (STI), stream power index (SPI), topographic wetness index (TWI), normalized difference vegetation index (NDVI), landslide, rainfall, soil and lithology. The conditioning factors selection and spatial correlation analysis were carried out by using the correlation attribute evaluation (CAE) method and the frequency ratio (FR) algorithm. The area under the receiver operating characteristic curve (AUROC) and kappa data of the training dataset and validation dataset are used to evaluate the prediction ability and the relationship between the advantages and disadvantages of landslide susceptibility maps. The results show that the SVR-GWO model (AUROC = 0.854) has the best performance in landslide spatial prediction, followed by the SVR-FA (AUROC = 0.838) and SVR models (AUROC = 0.818). The hybrid models of SVR-GWO and SVR-FA improve the performance of the single SVR model, and all three models have good prospects for regional-scale landslide spatial modeling.
Author Peng, Jianbing
Chen, Wei
Liu, Ru
Lee, Saro
Panahi, Mahdi
Zhao, Xia
Leng, Yanqiu
Author_xml – sequence: 1
  givenname: Ru
  surname: Liu
  fullname: Liu, Ru
– sequence: 2
  givenname: Jianbing
  surname: Peng
  fullname: Peng, Jianbing
– sequence: 3
  givenname: Yanqiu
  surname: Leng
  fullname: Leng, Yanqiu
– sequence: 4
  givenname: Saro
  orcidid: 0000-0003-0409-8263
  surname: Lee
  fullname: Lee, Saro
– sequence: 5
  givenname: Mahdi
  surname: Panahi
  fullname: Panahi, Mahdi
– sequence: 6
  givenname: Wei
  surname: Chen
  fullname: Chen, Wei
– sequence: 7
  givenname: Xia
  surname: Zhao
  fullname: Zhao, Xia
BookMark eNptkduKFDEQhoOs4Dq7Nz5BwBsRZk3n0OlcLot7gIGVPehlU-kkY4ZMp00ySPsAPreZHVFZDIRUwvf_lap6jY7GOFqE3jTkjDFFPqTcMMq5atsX6JgSSZecKnr0T_wKnea8IXUx1ijCj9HP61knbzKODt_vpimmgj_bocSE7-w62Zx9HPF3X77iq2Rn_CUGh2-n4rf-h00YRoMvfbIuzPg8rGOq4Ba7qr6foHgI-FOyxg9l71JTrKogB29sTZYHW320D77MJ-ilg5Dt6e9zgR4vPz5cXC9Xt1c3F-er5cAUL0sGjXHK0UbqWnFHOwmNFFJyACJaTTkF12prHCinDEhbr0YNUmrujADHFujm4GsibPop-S2kuY_g-6eHmNY9pOKHYHsOVhOieWuY4ERYEJozB5Ip2ZlOi-r17uA1pfhtZ3Ppt77WFAKMNu5yT1vWdqRuVdG3z9BN3KWxVlqphnZC0DqRBXp_oIYUc65N_fPBhvT7Cfd_J1xh8gwefIF9n0sCH_4n-QUCvawo
CitedBy_id crossref_primary_10_3390_rs14081919
crossref_primary_10_3390_su141711092
crossref_primary_10_1007_s11069_023_06176_7
crossref_primary_10_1155_2022_1511479
crossref_primary_10_1007_s00477_025_02911_7
crossref_primary_10_1007_s42452_025_06498_0
crossref_primary_10_1007_s11069_024_06673_3
crossref_primary_10_1007_s11069_023_06121_8
crossref_primary_10_1007_s11069_022_05520_7
crossref_primary_10_1007_s11600_023_01072_x
crossref_primary_10_1080_02723646_2025_2541644
crossref_primary_10_1016_j_envpol_2022_120203
crossref_primary_10_3390_ma18184274
crossref_primary_10_1002_gj_5175
crossref_primary_10_2166_wcc_2022_257
crossref_primary_10_2166_ws_2025_007
crossref_primary_10_3390_w15030453
crossref_primary_10_1007_s42979_024_02790_9
crossref_primary_10_3390_app122412887
crossref_primary_10_1007_s11227_023_05681_7
crossref_primary_10_3390_rs15041007
crossref_primary_10_1007_s40808_024_02157_2
crossref_primary_10_1007_s12517_022_09974_8
crossref_primary_10_3390_rs15153901
Cites_doi 10.1016/j.catena.2011.01.014
10.1007/s12665-010-0687-z
10.1016/j.asej.2016.08.023
10.1007/s12665-017-6731-5
10.1016/j.advengsoft.2013.12.007
10.1016/j.isatra.2017.08.009
10.1023/B:NHAZ.0000007097.42735.9e
10.3390/rs13194011
10.1007/s10346-006-0047-y
10.1007/s11069-012-0347-6
10.1007/s11069-012-0163-z
10.1016/j.catena.2007.01.003
10.1016/j.catena.2018.03.003
10.1016/j.rse.2014.05.013
10.1007/s12665-010-0705-1
10.1007/s11069-010-9598-2
10.1007/s40710-017-0248-5
10.1016/j.jhydrol.2016.06.027
10.1016/j.geomorph.2014.02.003
10.1007/s11707-017-0635-2
10.1016/j.scitotenv.2020.137231
10.1016/j.ecoleng.2017.08.010
10.1007/s11069-008-9305-8
10.1016/j.jseaes.2012.12.014
10.1016/j.cageo.2011.05.010
10.1016/j.catena.2016.06.004
10.1016/j.catena.2015.10.010
10.1007/s12665-014-3718-3
10.1016/j.catena.2012.05.005
10.1016/j.catena.2015.08.007
10.1007/s12665-015-5233-6
10.1007/s12517-012-0526-5
10.3390/su11164386
10.1016/j.jseaes.2009.01.002
10.1007/s12665-018-7268-y
10.1016/j.jhydrol.2020.125033
10.3390/app9214715
10.1007/978-981-10-5221-7
10.1016/j.geomorph.2009.10.002
10.5194/nhess-16-2729-2016
10.3390/s19214698
10.17485/ijst/2018/v11i12/99745
10.1007/s12665-018-7844-1
10.1007/s12040-012-0230-6
10.1016/j.geomorph.2020.107432
10.1016/j.geomorph.2017.09.007
10.1016/j.geomorph.2017.06.013
10.2307/2529310
10.1016/j.enggeo.2011.09.011
10.3390/rs8040347
10.1007/s12040-015-0624-3
10.1007/s12665-010-0531-5
10.1080/10106049.2017.1323964
10.1016/j.cageo.2010.10.012
10.1016/j.geomorph.2006.03.041
10.1016/j.scitotenv.2020.139937
10.1007/s10346-014-0521-x
10.1016/j.geomorph.2005.07.005
10.1016/j.envsoft.2004.11.013
10.1007/s11069-021-04844-0
10.3390/en12020289
10.1111/j.1467-9671.2005.00229.x
10.1007/s11069-018-3536-0
10.1016/j.jafrearsci.2016.02.019
10.1007/s10346-018-0950-z
10.1016/j.geomorph.2004.06.010
10.1080/19475705.2012.662915
10.1016/j.enggeo.2005.02.002
10.1016/j.catena.2015.05.019
10.1016/j.neunet.2013.01.008
10.1007/s10346-015-0614-1
10.1007/978-981-15-0306-1
10.1016/j.catena.2011.11.014
10.3390/su11051362
10.1016/j.cageo.2012.08.023
10.1007/s12665-017-6839-7
10.1016/j.asoc.2015.03.041
10.1016/j.scitotenv.2018.01.266
10.1145/130385.130401
10.1016/j.catena.2019.104364
10.1016/j.catena.2016.01.022
10.1016/S0169-555X(02)00176-9
10.1016/j.geomorph.2018.06.006
10.3390/e19080396
10.1007/s12665-017-7177-5
10.3390/rs11161943
10.1016/j.ijleo.2016.11.173
10.1016/S0167-8809(01)00187-6
10.3390/rs12142180
10.1023/B:NHAZ.0000026786.85589.4a
10.1007/s12517-017-2918-z
10.1016/j.ecolmodel.2011.12.007
10.1007/s12145-015-0220-8
10.1007/s12665-017-6981-2
10.1016/S1672-6529(09)60240-7
10.1007/s11069-012-0217-2
10.1016/j.beproc.2011.09.006
10.1016/j.cageo.2017.11.019
10.1155/2015/137695
10.1007/s12665-016-5919-4
10.1007/s00254-008-1342-9
10.1007/s13762-013-0464-0
10.1016/S0013-7952(03)00143-1
10.1007/s00254-007-0818-3
10.1016/j.enggeo.2006.05.001
10.1016/j.geoderma.2015.11.028
10.1007/s005310050149
10.1007/s11629-015-3464-3
10.1016/j.geomorph.2017.10.006
10.1016/j.scs.2017.08.004
10.1080/14498596.2018.1505564
10.5194/hess-22-4771-2018
10.1038/s41598-019-48773-2
10.1016/j.scitotenv.2018.01.124
10.1007/s11069-006-9027-8
10.3390/e21020218
10.1007/s11069-014-1378-y
10.1016/j.asoc.2016.12.022
10.1080/19475705.2018.1487471
10.1007/s12594-013-0162-z
10.1016/j.geoderma.2017.06.020
10.1016/j.geomorph.2016.02.012
10.1108/09653561011052547
10.1016/j.cageo.2008.08.007
10.1186/s40677-016-0053-x
10.1016/j.enggeo.2008.03.009
10.1007/s12524-010-0020-z
10.1080/13658810410001702003
10.1016/j.envsoft.2017.08.003
10.1016/j.jseaes.2012.10.005
10.1007/s12517-012-0532-7
10.1016/S0169-555X(02)00079-X
10.1016/j.catena.2015.07.020
10.1016/0034-4257(94)00071-T
10.1016/j.jhydrol.2010.12.027
10.1016/j.catena.2013.08.006
10.1016/j.catena.2020.104833
10.1016/j.catena.2017.11.022
10.1007/s12303-018-0052-x
10.1007/s10346-017-0883-y
10.1007/s00366-017-0535-9
10.1007/s10346-016-0711-9
10.1007/s11069-014-1245-x
10.1016/j.geoderma.2018.05.027
10.1080/19475705.2018.1481147
10.1007/s11629-017-4404-1
10.1016/j.molliq.2018.04.070
10.1016/j.catena.2019.104396
10.1080/19475705.2015.1115431
10.2166/nh.2017.044
10.1007/s00704-015-1702-9
10.3390/app7101000
10.1007/s12205-018-1337-3
10.1016/j.catena.2019.104225
10.1007/s12665-017-6558-0
10.1016/j.catena.2013.11.014
10.1007/s10346-011-0308-2
10.3390/rs10101545
ContentType Journal Article
Copyright 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID AAYXX
CITATION
7QF
7QO
7QQ
7QR
7SC
7SE
7SN
7SP
7SR
7TA
7TB
7U5
8BQ
8FD
8FE
8FG
ABJCF
ABUWG
AFKRA
ARAPS
AZQEC
BENPR
BGLVJ
BHPHI
BKSAR
C1K
CCPQU
DWQXO
F28
FR3
H8D
H8G
HCIFZ
JG9
JQ2
KR7
L6V
L7M
L~C
L~D
M7S
P5Z
P62
P64
PCBAR
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PTHSS
7S9
L.6
DOA
DOI 10.3390/rs13244966
DatabaseName CrossRef
Aluminium Industry Abstracts
Biotechnology Research Abstracts
Ceramic Abstracts
Chemoreception Abstracts
Computer and Information Systems Abstracts
Corrosion Abstracts
Ecology Abstracts
Electronics & Communications Abstracts
Engineered Materials Abstracts
Materials Business File
Mechanical & Transportation Engineering Abstracts
Solid State and Superconductivity Abstracts
METADEX
Technology Research Database
ProQuest SciTech Collection
ProQuest Technology Collection
Materials Science & Engineering Collection
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
Advanced Technologies & Computer Science Collection
ProQuest Central Essentials
ProQuest Central
Technology collection
ProQuest Natural Science Collection
Earth, Atmospheric & Aquatic Science Collection
Environmental Sciences and Pollution Management
ProQuest One Community College
ProQuest Central
ANTE: Abstracts in New Technology & Engineering
Engineering Research Database
Aerospace Database
Copper Technical Reference Library
SciTech Premium Collection
Materials Research Database
ProQuest Computer Science Collection
Civil Engineering Abstracts
ProQuest Engineering Collection
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
Engineering Database
Advanced Technologies & Aerospace Database
ProQuest Advanced Technologies & Aerospace Collection
Biotechnology and BioEngineering Abstracts
Earth, Atmospheric & Aquatic Science Database
ProQuest Databases
ProQuest One Academic
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
One Applied & Life Sciences
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
Engineering Collection
AGRICOLA
AGRICOLA - Academic
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
Publicly Available Content Database
Materials Research Database
ProQuest Advanced Technologies & Aerospace Collection
ProQuest Central Essentials
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
SciTech Premium Collection
Materials Business File
Environmental Sciences and Pollution Management
ProQuest One Applied & Life Sciences
Engineered Materials Abstracts
Natural Science Collection
Chemoreception Abstracts
ProQuest Central (New)
Engineering Collection
ANTE: Abstracts in New Technology & Engineering
Advanced Technologies & Aerospace Collection
Engineering Database
Aluminium Industry Abstracts
ProQuest One Academic Eastern Edition
Electronics & Communications Abstracts
Earth, Atmospheric & Aquatic Science Database
ProQuest Technology Collection
Ceramic Abstracts
Ecology Abstracts
Biotechnology and BioEngineering Abstracts
ProQuest One Academic UKI Edition
Solid State and Superconductivity Abstracts
Engineering Research Database
ProQuest One Academic
ProQuest One Academic (New)
Technology Collection
Technology Research Database
Computer and Information Systems Abstracts – Academic
ProQuest One Academic Middle East (New)
Mechanical & Transportation Engineering Abstracts
ProQuest Central (Alumni Edition)
ProQuest One Community College
Earth, Atmospheric & Aquatic Science Collection
ProQuest Central
Aerospace Database
Copper Technical Reference Library
ProQuest Engineering Collection
Biotechnology Research Abstracts
ProQuest Central Korea
Advanced Technologies Database with Aerospace
Civil Engineering Abstracts
ProQuest SciTech Collection
METADEX
Computer and Information Systems Abstracts Professional
Advanced Technologies & Aerospace Database
Materials Science & Engineering Collection
Corrosion Abstracts
AGRICOLA
AGRICOLA - Academic
DatabaseTitleList AGRICOLA
CrossRef
Publicly Available Content Database
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Geography
Geology
EISSN 2072-4292
ExternalDocumentID oai_doaj_org_article_4aeb00b46d35405ea5b43fa73978d8b5
10_3390_rs13244966
GroupedDBID 29P
2WC
2XV
5VS
8FE
8FG
8FH
AADQD
AAHBH
AAYXX
ABDBF
ABJCF
ACUHS
ADBBV
ADMLS
AENEX
AFFHD
AFKRA
AFZYC
ALMA_UNASSIGNED_HOLDINGS
ARAPS
BCNDV
BENPR
BGLVJ
BHPHI
BKSAR
CCPQU
CITATION
E3Z
ESX
FRP
GROUPED_DOAJ
HCIFZ
I-F
IAO
ITC
KQ8
L6V
LK5
M7R
M7S
MODMG
M~E
OK1
P62
PCBAR
PHGZM
PHGZT
PIMPY
PQGLB
PROAC
PTHSS
TR2
TUS
7QF
7QO
7QQ
7QR
7SC
7SE
7SN
7SP
7SR
7TA
7TB
7U5
8BQ
8FD
ABUWG
AZQEC
C1K
DWQXO
F28
FR3
H8D
H8G
JG9
JQ2
KR7
L7M
L~C
L~D
P64
PKEHL
PQEST
PQQKQ
PQUKI
7S9
L.6
PUEGO
ID FETCH-LOGICAL-c394t-3a1df9f217b3398287a175774aa056b242af6bedfa9f9da7eaf6d9c77b4fd5af3
IEDL.DBID DOA
ISICitedReferencesCount 22
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000737367900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2072-4292
IngestDate Tue Oct 14 19:05:06 EDT 2025
Fri Sep 05 11:46:36 EDT 2025
Fri Jul 25 11:52:47 EDT 2025
Sat Nov 29 07:14:17 EST 2025
Tue Nov 18 21:06:59 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 24
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c394t-3a1df9f217b3398287a175774aa056b242af6bedfa9f9da7eaf6d9c77b4fd5af3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ORCID 0000-0003-0409-8263
OpenAccessLink https://doaj.org/article/4aeb00b46d35405ea5b43fa73978d8b5
PQID 2612855203
PQPubID 2032338
ParticipantIDs doaj_primary_oai_doaj_org_article_4aeb00b46d35405ea5b43fa73978d8b5
proquest_miscellaneous_2636803689
proquest_journals_2612855203
crossref_primary_10_3390_rs13244966
crossref_citationtrail_10_3390_rs13244966
PublicationCentury 2000
PublicationDate 2021-12-01
PublicationDateYYYYMMDD 2021-12-01
PublicationDate_xml – month: 12
  year: 2021
  text: 2021-12-01
  day: 01
PublicationDecade 2020
PublicationPlace Basel
PublicationPlace_xml – name: Basel
PublicationTitle Remote sensing (Basel, Switzerland)
PublicationYear 2021
Publisher MDPI AG
Publisher_xml – name: MDPI AG
References Pradhan (ref_112) 2012; 171
Zhiwen (ref_142) 2017; 71
Pham (ref_102) 2017; 4
Youssef (ref_91) 2016; 13
ref_139
Choi (ref_19) 2012; 124
Yesilnacar (ref_111) 2005; 79
ref_138
Sujatha (ref_24) 2012; 121
Rozos (ref_39) 2011; 63
Hong (ref_64) 2017; 76
Miao (ref_93) 2018; 15
ref_13
ref_131
ref_130
Pourghasemi (ref_26) 2013; 6
Ayalew (ref_50) 2005; 65
ref_132
Rasyid (ref_8) 2016; 3
Pradhan (ref_100) 2016; 140
Mohammady (ref_41) 2012; 61
Steger (ref_61) 2016; 16
Lai (ref_78) 2018; 49
Hong (ref_67) 2015; 133
Oh (ref_96) 2009; 57
ref_16
Pourghasemi (ref_35) 2012; 63
Feizizadeh (ref_74) 2017; 10
Shahabi (ref_153) 2014; 115
Fell (ref_166) 2008; 102
Lee (ref_30) 2004; 18
Oh (ref_156) 2018; 9
Aditian (ref_10) 2018; 318
ref_129
Pham (ref_5) 2017; 128
Huang (ref_86) 2018; 165
Visser (ref_162) 2006; 21
ref_22
ref_122
ref_121
Garosi (ref_90) 2018; 330
Pradhan (ref_137) 2018; 9
Chang (ref_107) 2019; 9
Dahal (ref_108) 2008; 54
Westen (ref_15) 1997; 86
Zang (ref_150) 2010; 7
Yalcin (ref_40) 2008; 72
Rengers (ref_47) 2003; 30
ref_72
ref_159
ref_70
Zhang (ref_141) 2016; 130
Camilo (ref_92) 2017; 97
Asl (ref_149) 2017; 34
ref_152
Chen (ref_167) 2017; 297
ref_155
Irigaray (ref_52) 2009; 50
Jaafari (ref_117) 2014; 11
Ho (ref_164) 2016; 75
Sarkar (ref_46) 2013; 82
Alexandridis (ref_151) 2013; 42
Arabameri (ref_79) 2017; 76
Pradhan (ref_21) 2010; 38
Pourghasemi (ref_42) 2013; 4
Colkesen (ref_88) 2016; 118
Gorsevski (ref_45) 2005; 9
Lorang (ref_105) 2002; 48
Muro (ref_143) 2011; 88
ref_148
Sahana (ref_60) 2017; 14
Sahoo (ref_136) 2016; 52
Meten (ref_58) 2015; 12
ref_140
ref_89
Kayastha (ref_28) 2012; 63
Irigaray (ref_51) 2007; 41
ref_146
Panahi (ref_53) 2020; 741
ref_84
ref_145
Tsangaratos (ref_80) 2014; 74
Tian (ref_81) 2019; 10
Pontius (ref_161) 2001; 85
Ercanoglu (ref_7) 2004; 32
Pham (ref_1) 2018; 77
Zhang (ref_71) 2017; 76
Pourghasemi (ref_109) 2018; 162
Thomas (ref_125) 2018; 15
Chen (ref_160) 2017; 305
Doyuran (ref_12) 2004; 71
Saboya (ref_34) 2006; 86
Pradhan (ref_32) 2011; 63
Dehnavi (ref_54) 2015; 135
Hong (ref_99) 2020; 718
Conforti (ref_115) 2011; 56
Devkota (ref_25) 2013; 65
Panahi (ref_133) 2020; 588
Khosravi (ref_103) 2018; 627
Wang (ref_18) 2014; 72
Yilmaz (ref_20) 2009; 35
Zhu (ref_36) 2014; 214
Vorpahl (ref_116) 2012; 239
Kim (ref_76) 2018; 33
Razavizadeh (ref_95) 2017; 76
Rahmati (ref_106) 2016; 137
Aghdam (ref_57) 2017; 76
Landis (ref_165) 1977; 33
Ghorbanzadeh (ref_56) 2020; 65
Razandi (ref_17) 2015; 8
Wu (ref_157) 2020; 187
Conforti (ref_82) 2014; 113
Khosravi (ref_94) 2018; 22
Oh (ref_126) 2011; 399
Clerici (ref_11) 2002; 48
Jebur (ref_27) 2014; 152
Komac (ref_38) 2006; 74
Singh (ref_168) 2010; 19
ref_69
Solaimani (ref_97) 2013; 6
ref_68
Feng (ref_65) 2018; 22
Lee (ref_127) 2007; 4
Hong (ref_154) 2019; 96
Zhou (ref_3) 2018; 112
Wei (ref_98) 2021; 109
Che (ref_49) 2012; 92
Chen (ref_128) 2021; 196
Sulaiman (ref_135) 2015; 32
Avelar (ref_114) 2007; 87
Meliho (ref_163) 2018; 77
Aghdam (ref_55) 2016; 75
Ahmed (ref_37) 2015; 12
Kumar (ref_85) 2017; 295
Yalcin (ref_113) 2011; 85
Pham (ref_66) 2018; 11
Mahalingam (ref_4) 2015; 7
Kadirhodjaev (ref_77) 2018; 22
Wang (ref_23) 2015; 124
Tuan (ref_73) 2017; 14
Wang (ref_62) 2015; 135
Aksoy (ref_33) 2012; 38
Nassar (ref_59) 2018; 11
Tsangaratos (ref_63) 2016; 145
Pourghasemi (ref_104) 2020; 187
Hall (ref_118) 1995; 51
Rossi (ref_123) 2017; 109
Pourghasemi (ref_158) 2012; 97
Chen (ref_169) 2018; 626
Pradhan (ref_14) 2013; 51
Hong (ref_75) 2016; 259
Zhao (ref_119) 2015; 2015
Sharma (ref_48) 2015; 75
Micu (ref_9) 2020; 371
Park (ref_43) 2011; 62
Martelloni (ref_120) 2012; 9
Yuan (ref_101) 2017; 11
Spross (ref_83) 2019; 183
Ozdemir (ref_29) 2013; 64
Oh (ref_110) 2011; 37
Cheng (ref_124) 2016; 265
Mirjalili (ref_134) 2014; 69
ref_2
Jiang (ref_147) 2017; 35
Regmi (ref_31) 2010; 115
Pradhan (ref_87) 2016; 540
Tangestani (ref_44) 2009; 35
Bian (ref_144) 2018; 261
ref_6
References_xml – volume: 85
  start-page: 274
  year: 2011
  ident: ref_113
  article-title: A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey
  publication-title: Catena
  doi: 10.1016/j.catena.2011.01.014
– volume: 63
  start-page: 49
  year: 2011
  ident: ref_39
  article-title: Comparison of the implementation of rock engineering system and analytic hierarchy process methods, upon landslide susceptibility mapping, using GIS: A case study from the Eastern Achaia County of Peloponnesus, Greece
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-010-0687-z
– volume: 9
  start-page: 2015
  year: 2018
  ident: ref_137
  article-title: Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system
  publication-title: Ain Shams Eng. J.
  doi: 10.1016/j.asej.2016.08.023
– volume: 76
  start-page: 405
  year: 2017
  ident: ref_71
  article-title: The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-017-6731-5
– volume: 69
  start-page: 46
  year: 2014
  ident: ref_134
  article-title: Grey Wolf Optimizer
  publication-title: Adv. Eng. Softw.
  doi: 10.1016/j.advengsoft.2013.12.007
– volume: 71
  start-page: 206
  year: 2017
  ident: ref_142
  article-title: An adaptive stochastic resonance method based on grey wolf optimizer algorithm and its application to machinery fault diagnosis
  publication-title: ISA Trans.
  doi: 10.1016/j.isatra.2017.08.009
– volume: 30
  start-page: 399
  year: 2003
  ident: ref_47
  article-title: Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment
  publication-title: Nat. Hazards
  doi: 10.1023/B:NHAZ.0000007097.42735.9e
– ident: ref_13
  doi: 10.3390/rs13194011
– volume: 4
  start-page: 33
  year: 2007
  ident: ref_127
  article-title: Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models
  publication-title: Landslides
  doi: 10.1007/s10346-006-0047-y
– ident: ref_155
– volume: 65
  start-page: 135
  year: 2013
  ident: ref_25
  article-title: Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya
  publication-title: Nat. Hazards
  doi: 10.1007/s11069-012-0347-6
– volume: 63
  start-page: 479
  year: 2012
  ident: ref_28
  article-title: Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal
  publication-title: Nat. Hazards
  doi: 10.1007/s11069-012-0163-z
– ident: ref_132
– volume: 72
  start-page: 1
  year: 2008
  ident: ref_40
  article-title: GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations
  publication-title: Catena
  doi: 10.1016/j.catena.2007.01.003
– volume: 165
  start-page: 520
  year: 2018
  ident: ref_86
  article-title: Review on landslide susceptibility mapping using support vector machines
  publication-title: Catena
  doi: 10.1016/j.catena.2018.03.003
– volume: 152
  start-page: 150
  year: 2014
  ident: ref_27
  article-title: Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale
  publication-title: Remote Sens. Environ.
  doi: 10.1016/j.rse.2014.05.013
– volume: 63
  start-page: 329
  year: 2011
  ident: ref_32
  article-title: Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-010-0705-1
– volume: 56
  start-page: 881
  year: 2011
  ident: ref_115
  article-title: Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy)
  publication-title: Nat. Hazards
  doi: 10.1007/s11069-010-9598-2
– volume: 4
  start-page: 711
  year: 2017
  ident: ref_102
  article-title: Application and comparison of decision tree-based machine learning methods in landside susceptibility assessment at Pauri Garhwal Area, Uttarakhand, India
  publication-title: Environ. Process.
  doi: 10.1007/s40710-017-0248-5
– volume: 540
  start-page: 317
  year: 2016
  ident: ref_87
  article-title: Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS
  publication-title: J. Hydrol.
  doi: 10.1016/j.jhydrol.2016.06.027
– volume: 214
  start-page: 128
  year: 2014
  ident: ref_36
  article-title: An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic
  publication-title: Geomorphology
  doi: 10.1016/j.geomorph.2014.02.003
– volume: 11
  start-page: 202
  year: 2017
  ident: ref_101
  article-title: Erratum to: Newmark displacement model for landslides induced by the 2013 Ms 7.0 Lushan earthquake, China
  publication-title: Front. Earth Sci.
  doi: 10.1007/s11707-017-0635-2
– volume: 718
  start-page: 137231
  year: 2020
  ident: ref_99
  article-title: Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2020.137231
– volume: 109
  start-page: 249
  year: 2017
  ident: ref_123
  article-title: Sensitivity of the landslide model LAPSUS_LS to vegetation and soil parameters
  publication-title: Ecol. Eng.
  doi: 10.1016/j.ecoleng.2017.08.010
– volume: 50
  start-page: 571
  year: 2009
  ident: ref_52
  article-title: Building models for automatic landslide-susceptibility analysis, mapping and validation in ArcGIS
  publication-title: Nat. Hazards
  doi: 10.1007/s11069-008-9305-8
– volume: 64
  start-page: 180
  year: 2013
  ident: ref_29
  article-title: A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey
  publication-title: J. Asian Earth Sci.
  doi: 10.1016/j.jseaes.2012.12.014
– volume: 38
  start-page: 87
  year: 2012
  ident: ref_33
  article-title: Landslide identification and classification by object-based image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey)
  publication-title: Comput. Geosci.
  doi: 10.1016/j.cageo.2011.05.010
– volume: 145
  start-page: 164
  year: 2016
  ident: ref_63
  article-title: Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size
  publication-title: Catena
  doi: 10.1016/j.catena.2016.06.004
– volume: 137
  start-page: 360
  year: 2016
  ident: ref_106
  article-title: Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran
  publication-title: Catena
  doi: 10.1016/j.catena.2015.10.010
– volume: 72
  start-page: 4639
  year: 2014
  ident: ref_18
  article-title: Comparison of rockfall susceptibility assessment at local and regional scale: A case study in the north of Beijing (China)
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-014-3718-3
– volume: 97
  start-page: 71
  year: 2012
  ident: ref_158
  article-title: Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran
  publication-title: Catena
  doi: 10.1016/j.catena.2012.05.005
– volume: 135
  start-page: 271
  year: 2015
  ident: ref_62
  article-title: Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models
  publication-title: Catena
  doi: 10.1016/j.catena.2015.08.007
– volume: 75
  start-page: 553
  year: 2016
  ident: ref_55
  article-title: Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran)
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-015-5233-6
– volume: 6
  start-page: 2557
  year: 2013
  ident: ref_97
  article-title: Landslide susceptibility mapping based on frequency ratio and logistic regression models
  publication-title: Arab. J. Geosci.
  doi: 10.1007/s12517-012-0526-5
– ident: ref_152
– ident: ref_68
  doi: 10.3390/su11164386
– volume: 35
  start-page: 66
  year: 2009
  ident: ref_44
  article-title: A comparative study of Dempster–Shafer and fuzzy models for landslide susceptibility mapping using a GIS: An experience from Zagros Mountains, SW Iran
  publication-title: J. Asian Earth Sci.
  doi: 10.1016/j.jseaes.2009.01.002
– volume: 77
  start-page: 146
  year: 2018
  ident: ref_1
  article-title: Bagging based Support Vector Machines for spatial prediction of landslides
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-018-7268-y
– volume: 588
  start-page: 125033
  year: 2020
  ident: ref_133
  article-title: Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)
  publication-title: J. Hydrol.
  doi: 10.1016/j.jhydrol.2020.125033
– ident: ref_146
  doi: 10.3390/app9214715
– ident: ref_139
  doi: 10.1007/978-981-10-5221-7
– volume: 115
  start-page: 172
  year: 2010
  ident: ref_31
  article-title: Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA
  publication-title: Geomorphology
  doi: 10.1016/j.geomorph.2009.10.002
– volume: 16
  start-page: 2729
  year: 2016
  ident: ref_61
  article-title: The propagation of inventory-based positional errors into statistical landslide susceptibility models
  publication-title: Nat. Hazards Earth Syst. Sci.
  doi: 10.5194/nhess-16-2729-2016
– ident: ref_138
  doi: 10.3390/s19214698
– volume: 11
  start-page: 1
  year: 2018
  ident: ref_66
  article-title: Machine Learning Methods of Kernel Logistic Regression and Classification and Regression Trees for Landslide Susceptibility Assessment at Part of Himalayan Area, India
  publication-title: Indian J. Sci. Technol.
  doi: 10.17485/ijst/2018/v11i12/99745
– volume: 77
  start-page: 655
  year: 2018
  ident: ref_163
  article-title: A GIS-based approach for gully erosion susceptibility modelling using bivariate statistics methods in the Ourika watershed, Morocco
  publication-title: Environ. Earth Ences
  doi: 10.1007/s12665-018-7844-1
– volume: 121
  start-page: 1337
  year: 2012
  ident: ref_24
  article-title: Landslide susceptibility analysis using Probabilistic Certainty Factor Approach: A case study on Tevankarai stream watershed, India
  publication-title: J. Earth Syst. Sci.
  doi: 10.1007/s12040-012-0230-6
– volume: 371
  start-page: 107432
  year: 2020
  ident: ref_9
  article-title: National-scale landslide susceptibility map of Romania in a European methodological framework
  publication-title: Geomorphology
  doi: 10.1016/j.geomorph.2020.107432
– volume: 297
  start-page: 69
  year: 2017
  ident: ref_167
  article-title: Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques
  publication-title: Geomorphology
  doi: 10.1016/j.geomorph.2017.09.007
– volume: 295
  start-page: 115
  year: 2017
  ident: ref_85
  article-title: Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India
  publication-title: Geomorphology
  doi: 10.1016/j.geomorph.2017.06.013
– ident: ref_129
– volume: 33
  start-page: 159
  year: 1977
  ident: ref_165
  article-title: JSTOR: Biometrics
  publication-title: Biometrics
  doi: 10.2307/2529310
– volume: 124
  start-page: 12
  year: 2012
  ident: ref_19
  article-title: Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS
  publication-title: Eng. Geol.
  doi: 10.1016/j.enggeo.2011.09.011
– ident: ref_69
  doi: 10.3390/rs8040347
– volume: 124
  start-page: 1399
  year: 2015
  ident: ref_23
  article-title: GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China
  publication-title: J. Earth Syst. Sci.
  doi: 10.1007/s12040-015-0624-3
– volume: 62
  start-page: 367
  year: 2011
  ident: ref_43
  article-title: Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-010-0531-5
– volume: 33
  start-page: 1000
  year: 2018
  ident: ref_76
  article-title: Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea
  publication-title: Geocarto Int.
  doi: 10.1080/10106049.2017.1323964
– volume: 37
  start-page: 1264
  year: 2011
  ident: ref_110
  article-title: Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area
  publication-title: Comput. Geosci.
  doi: 10.1016/j.cageo.2010.10.012
– volume: 87
  start-page: 120
  year: 2007
  ident: ref_114
  article-title: Landslide susceptibility in a mountainous geoecosystem, Tijuca Massif, Rio de Janeiro: The role of morphometric subdivision of the terrain
  publication-title: Geomorphology
  doi: 10.1016/j.geomorph.2006.03.041
– ident: ref_6
– volume: 741
  start-page: 139937
  year: 2020
  ident: ref_53
  article-title: Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2020.139937
– volume: 12
  start-page: 1077
  year: 2015
  ident: ref_37
  article-title: Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh
  publication-title: Landslides
  doi: 10.1007/s10346-014-0521-x
– volume: 74
  start-page: 17
  year: 2006
  ident: ref_38
  article-title: A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia
  publication-title: Geomorphology
  doi: 10.1016/j.geomorph.2005.07.005
– volume: 21
  start-page: 346
  year: 2006
  ident: ref_162
  article-title: The Map Comparison Kit
  publication-title: Environ. Model. Softw.
  doi: 10.1016/j.envsoft.2004.11.013
– volume: 109
  start-page: 471
  year: 2021
  ident: ref_98
  article-title: A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping
  publication-title: Nat. Hazards
  doi: 10.1007/s11069-021-04844-0
– ident: ref_140
  doi: 10.3390/en12020289
– volume: 9
  start-page: 455
  year: 2005
  ident: ref_45
  article-title: Spatial Prediction of Landslide Hazard Using Fuzzy k-means and Dempster-Shafer Theory
  publication-title: Trans. GIS
  doi: 10.1111/j.1467-9671.2005.00229.x
– volume: 96
  start-page: 173
  year: 2019
  ident: ref_154
  article-title: Landslide susceptibility assessment at the Wuning area, China: A comparison between multi-criteria decision making, bivariate statistical and machine learning methods
  publication-title: Nat. Hazards
  doi: 10.1007/s11069-018-3536-0
– volume: 118
  start-page: 53
  year: 2016
  ident: ref_88
  article-title: Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression
  publication-title: J. Afr. Earth Sci.
  doi: 10.1016/j.jafrearsci.2016.02.019
– volume: 15
  start-page: 1265
  year: 2018
  ident: ref_125
  article-title: Variability in soil-water retention properties and implications for physics-based simulation of landslide early warning criteria
  publication-title: Landslides
  doi: 10.1007/s10346-018-0950-z
– volume: 65
  start-page: 15
  year: 2005
  ident: ref_50
  article-title: The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan
  publication-title: Geomorphology
  doi: 10.1016/j.geomorph.2004.06.010
– ident: ref_22
– volume: 4
  start-page: 93
  year: 2013
  ident: ref_42
  article-title: A comparative assessment of prediction capabilities of Dempster–Shafer and Weights-of-evidence models in landslide susceptibility mapping using GIS
  publication-title: Geomat. Nat. Hazards Risk
  doi: 10.1080/19475705.2012.662915
– volume: 79
  start-page: 251
  year: 2005
  ident: ref_111
  article-title: Landslide susceptibility mapping: A comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)
  publication-title: Eng. Geol.
  doi: 10.1016/j.enggeo.2005.02.002
– volume: 133
  start-page: 266
  year: 2015
  ident: ref_67
  article-title: Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines
  publication-title: Catena
  doi: 10.1016/j.catena.2015.05.019
– volume: 42
  start-page: 1
  year: 2013
  ident: ref_151
  article-title: Wavelet neural networks: A practical guide
  publication-title: Neural Netw.
  doi: 10.1016/j.neunet.2013.01.008
– volume: 13
  start-page: 839
  year: 2016
  ident: ref_91
  article-title: Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia
  publication-title: Landslides
  doi: 10.1007/s10346-015-0614-1
– ident: ref_148
  doi: 10.1007/978-981-15-0306-1
– volume: 92
  start-page: 83
  year: 2012
  ident: ref_49
  article-title: Landslide susceptibility assessment in Limbe (SW Cameroon): A field calibrated seed cell and information value method
  publication-title: Catena
  doi: 10.1016/j.catena.2011.11.014
– ident: ref_16
  doi: 10.3390/su11051362
– volume: 51
  start-page: 350
  year: 2013
  ident: ref_14
  article-title: A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS
  publication-title: Comput. Geosci.
  doi: 10.1016/j.cageo.2012.08.023
– volume: 76
  start-page: 499
  year: 2017
  ident: ref_95
  article-title: Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: A case study in northern Iran
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-017-6839-7
– volume: 32
  start-page: 286
  year: 2015
  ident: ref_135
  article-title: Using the gray wolf optimizer for solving optimal reactive power dispatch problem
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2015.03.041
– volume: 627
  start-page: 744
  year: 2018
  ident: ref_103
  article-title: A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2018.01.266
– ident: ref_131
  doi: 10.1145/130385.130401
– volume: 187
  start-page: 104364
  year: 2020
  ident: ref_104
  article-title: Investigating the effects of different landslide positioning techniques, landslide partitioning approaches, and presence-absence balances on landslide susceptibility mapping
  publication-title: Catena
  doi: 10.1016/j.catena.2019.104364
– volume: 140
  start-page: 125
  year: 2016
  ident: ref_100
  article-title: Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping
  publication-title: Catena
  doi: 10.1016/j.catena.2016.01.022
– volume: 48
  start-page: 87
  year: 2002
  ident: ref_105
  article-title: Predicting the crest height of a gravel beach
  publication-title: Geomorphology
  doi: 10.1016/S0169-555X(02)00176-9
– volume: 318
  start-page: 101
  year: 2018
  ident: ref_10
  article-title: Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia
  publication-title: Geomorphology
  doi: 10.1016/j.geomorph.2018.06.006
– ident: ref_72
  doi: 10.3390/e19080396
– volume: 76
  start-page: 20
  year: 2017
  ident: ref_79
  article-title: Applying different scenarios for landslide spatial modeling using computational intelligence methods
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-017-7177-5
– ident: ref_89
  doi: 10.3390/rs11161943
– volume: 130
  start-page: 1229
  year: 2016
  ident: ref_141
  article-title: Template Matching Using Grey Wolf Optimizer with Lateral Inhibition
  publication-title: Optik
  doi: 10.1016/j.ijleo.2016.11.173
– volume: 85
  start-page: 239
  year: 2001
  ident: ref_161
  article-title: Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA
  publication-title: Agric. Ecosyst. Environ.
  doi: 10.1016/S0167-8809(01)00187-6
– ident: ref_159
  doi: 10.3390/rs12142180
– volume: 32
  start-page: 1
  year: 2004
  ident: ref_7
  article-title: Landslide Susceptibility Zoning North of Yenice (NW Turkey) by Multivariate Statistical Techniques
  publication-title: Nat. Hazards
  doi: 10.1023/B:NHAZ.0000026786.85589.4a
– volume: 10
  start-page: 13
  year: 2017
  ident: ref_74
  article-title: Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping
  publication-title: Arab. J. Geosci.
  doi: 10.1007/s12517-017-2918-z
– volume: 239
  start-page: 27
  year: 2012
  ident: ref_116
  article-title: How can statistical models help to determine driving factors of landslides?
  publication-title: Ecol. Model.
  doi: 10.1016/j.ecolmodel.2011.12.007
– volume: 8
  start-page: 867
  year: 2015
  ident: ref_17
  article-title: Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS
  publication-title: Earth Sci. Inform.
  doi: 10.1007/s12145-015-0220-8
– volume: 76
  start-page: 652
  year: 2017
  ident: ref_64
  article-title: A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-017-6981-2
– volume: 7
  start-page: S232
  year: 2010
  ident: ref_150
  article-title: A Review of Nature-Inspired Algorithms
  publication-title: J. Bionic Eng.
  doi: 10.1016/S1672-6529(09)60240-7
– volume: 63
  start-page: 965
  year: 2012
  ident: ref_35
  article-title: Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran
  publication-title: Nat. Hazards
  doi: 10.1007/s11069-012-0217-2
– volume: 88
  start-page: 192
  year: 2011
  ident: ref_143
  article-title: Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations
  publication-title: Behav. Process.
  doi: 10.1016/j.beproc.2011.09.006
– volume: 112
  start-page: 23
  year: 2018
  ident: ref_3
  article-title: Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China
  publication-title: Comput. Geosci.
  doi: 10.1016/j.cageo.2017.11.019
– volume: 2015
  start-page: 137695
  year: 2015
  ident: ref_119
  article-title: Umbilical Cord-Derived Mesenchymal Stem Cells Inhibit Cadherin-11 Expression by Fibroblast-Like Synoviocytes in Rheumatoid Arthritis
  publication-title: J. Immunol. Res.
  doi: 10.1155/2015/137695
– volume: 75
  start-page: 1101
  year: 2016
  ident: ref_164
  article-title: GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-016-5919-4
– volume: 57
  start-page: 641
  year: 2009
  ident: ref_96
  article-title: Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand
  publication-title: Environ. Geol.
  doi: 10.1007/s00254-008-1342-9
– volume: 11
  start-page: 909
  year: 2014
  ident: ref_117
  article-title: GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran
  publication-title: Int. J. Environ. Sci. Technol.
  doi: 10.1007/s13762-013-0464-0
– volume: 71
  start-page: 303
  year: 2004
  ident: ref_12
  article-title: Data driven bivariate landslide susceptibility assessment using geographical information systems: A method and application to Asarsuyu catchment, Turkey
  publication-title: Eng. Geol.
  doi: 10.1016/S0013-7952(03)00143-1
– volume: 54
  start-page: 311
  year: 2008
  ident: ref_108
  article-title: GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping
  publication-title: Environ. Geol.
  doi: 10.1007/s00254-007-0818-3
– volume: 86
  start-page: 211
  year: 2006
  ident: ref_34
  article-title: Assessment of failure susceptibility of soil slopes using fuzzy logic
  publication-title: Eng. Geol.
  doi: 10.1016/j.enggeo.2006.05.001
– volume: 265
  start-page: 187
  year: 2016
  ident: ref_124
  article-title: Landslide-induced changes of soil physicochemical properties in Xitou, Central Taiwan
  publication-title: Geoderma
  doi: 10.1016/j.geoderma.2015.11.028
– volume: 86
  start-page: 404
  year: 1997
  ident: ref_15
  article-title: Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation
  publication-title: Geol. Rundsch.
  doi: 10.1007/s005310050149
– volume: 12
  start-page: 1355
  year: 2015
  ident: ref_58
  article-title: GIS-based frequency ratio and logistic regression modelling for landslide susceptibility mapping of Debre Sina area in central Ethiopia
  publication-title: J. Mt. Sci.
  doi: 10.1007/s11629-015-3464-3
– ident: ref_145
– ident: ref_122
  doi: 10.1016/j.geomorph.2017.10.006
– volume: 35
  start-page: 250
  year: 2017
  ident: ref_147
  article-title: Dynamic measurement errors prediction for sensors based on firefly algorithm optimize support vector machine
  publication-title: Sustain. Cities Soc.
  doi: 10.1016/j.scs.2017.08.004
– volume: 65
  start-page: 401
  year: 2020
  ident: ref_56
  article-title: A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping
  publication-title: J. Spat. Sci.
  doi: 10.1080/14498596.2018.1505564
– volume: 22
  start-page: 4771
  year: 2018
  ident: ref_94
  article-title: Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization
  publication-title: Hydrol. Earth Syst. Sci.
  doi: 10.5194/hess-22-4771-2018
– volume: 9
  start-page: 12296
  year: 2019
  ident: ref_107
  article-title: Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques
  publication-title: Sci. Rep.
  doi: 10.1038/s41598-019-48773-2
– volume: 626
  start-page: 1121
  year: 2018
  ident: ref_169
  article-title: Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China
  publication-title: Sci. Total Environ.
  doi: 10.1016/j.scitotenv.2018.01.124
– volume: 41
  start-page: 61
  year: 2007
  ident: ref_51
  article-title: Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: Examples from the Betic Cordillera (southern Spain)
  publication-title: Nat. Hazards
  doi: 10.1007/s11069-006-9027-8
– ident: ref_121
  doi: 10.3390/e21020218
– volume: 75
  start-page: 1555
  year: 2015
  ident: ref_48
  article-title: Development and application of Shannon’s entropy integrated information value model for landslide susceptibility assessment and zonation in Sikkim Himalayas in India
  publication-title: Nat. Hazards
  doi: 10.1007/s11069-014-1378-y
– volume: 52
  start-page: 64
  year: 2016
  ident: ref_136
  article-title: Multi-objective Grey Wolf Optimizer for improved cervix lesion classification
  publication-title: Appl. Soft Comput.
  doi: 10.1016/j.asoc.2016.12.022
– volume: 10
  start-page: 1
  year: 2019
  ident: ref_81
  article-title: Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: An example of the 2013 Minxian (China) Mw 5.9 event
  publication-title: Geomat. Nat. Hazards Risk
  doi: 10.1080/19475705.2018.1487471
– volume: 82
  start-page: 351
  year: 2013
  ident: ref_46
  article-title: Landslide susceptibility assessment using Information Value Method in parts of the Darjeeling Himalayas
  publication-title: J. Geol. Soc. India
  doi: 10.1007/s12594-013-0162-z
– volume: 171
  start-page: 12
  year: 2012
  ident: ref_112
  article-title: Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks
  publication-title: Geomorphology
– volume: 305
  start-page: 314
  year: 2017
  ident: ref_160
  article-title: Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques
  publication-title: Geoderma
  doi: 10.1016/j.geoderma.2017.06.020
– volume: 259
  start-page: 105
  year: 2016
  ident: ref_75
  article-title: Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models
  publication-title: Geomorphology
  doi: 10.1016/j.geomorph.2016.02.012
– volume: 19
  start-page: 384
  year: 2010
  ident: ref_168
  article-title: Bioengineering techniques of slope stabilization and landslide mitigation
  publication-title: Disaster Prev. Manag.
  doi: 10.1108/09653561011052547
– volume: 35
  start-page: 1125
  year: 2009
  ident: ref_20
  article-title: Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)
  publication-title: Comput. Geosci.
  doi: 10.1016/j.cageo.2008.08.007
– volume: 3
  start-page: 19
  year: 2016
  ident: ref_8
  article-title: Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia
  publication-title: Geoenviron. Disasters
  doi: 10.1186/s40677-016-0053-x
– volume: 11
  start-page: 10
  year: 2018
  ident: ref_59
  article-title: Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors
  publication-title: Arab. J. Geosci.
– volume: 102
  start-page: 83
  year: 2008
  ident: ref_166
  article-title: Guidelines for landslide susceptibility, hazard and risk zoning for land use planning—ScienceDirect
  publication-title: Eng. Geol.
  doi: 10.1016/j.enggeo.2008.03.009
– ident: ref_130
– volume: 38
  start-page: 301
  year: 2010
  ident: ref_21
  article-title: Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches
  publication-title: J. Indian Soc. Remote Sens.
  doi: 10.1007/s12524-010-0020-z
– volume: 18
  start-page: 789
  year: 2004
  ident: ref_30
  article-title: Landslide susceptibility mapping using GIS and the weight-of-evidence model
  publication-title: Int. J. Geogr. Inf. Sci.
  doi: 10.1080/13658810410001702003
– volume: 97
  start-page: 145
  year: 2017
  ident: ref_92
  article-title: Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model
  publication-title: Environ. Model. Softw.
  doi: 10.1016/j.envsoft.2017.08.003
– volume: 61
  start-page: 221
  year: 2012
  ident: ref_41
  article-title: Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models
  publication-title: J. Asian Earth Sci.
  doi: 10.1016/j.jseaes.2012.10.005
– volume: 6
  start-page: 2351
  year: 2013
  ident: ref_26
  article-title: Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran
  publication-title: Arab. J. Geosci.
  doi: 10.1007/s12517-012-0532-7
– volume: 48
  start-page: 349
  year: 2002
  ident: ref_11
  article-title: A procedure for landslide susceptibility zonation by the conditional analysis method
  publication-title: Geomorphology
  doi: 10.1016/S0169-555X(02)00079-X
– volume: 135
  start-page: 122
  year: 2015
  ident: ref_54
  article-title: A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran
  publication-title: Catena
  doi: 10.1016/j.catena.2015.07.020
– volume: 51
  start-page: 138
  year: 1995
  ident: ref_118
  article-title: Status of remote sensing algorithms for estimation of land surface state parameters
  publication-title: Remote Sens. Environ.
  doi: 10.1016/0034-4257(94)00071-T
– volume: 399
  start-page: 158
  year: 2011
  ident: ref_126
  article-title: GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea
  publication-title: J. Hydrol.
  doi: 10.1016/j.jhydrol.2010.12.027
– volume: 113
  start-page: 236
  year: 2014
  ident: ref_82
  article-title: Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy)
  publication-title: Catena
  doi: 10.1016/j.catena.2013.08.006
– volume: 196
  start-page: 104833
  year: 2021
  ident: ref_128
  article-title: Gis-based landslide susceptibility assessment using optimized hybrid machine learning methods
  publication-title: CATENA
  doi: 10.1016/j.catena.2020.104833
– ident: ref_2
– volume: 162
  start-page: 177
  year: 2018
  ident: ref_109
  article-title: Prediction of the landslide susceptibility: Which algorithm, which precision?
  publication-title: Catena
  doi: 10.1016/j.catena.2017.11.022
– volume: 22
  start-page: 1053
  year: 2018
  ident: ref_77
  article-title: Analysis of the relationships between topographic factors and landslide occurrence and their application to landslide susceptibility mapping: A case study of Mingchukur, Uzbekistan
  publication-title: Geosci. J.
  doi: 10.1007/s12303-018-0052-x
– volume: 15
  start-page: 475
  year: 2018
  ident: ref_93
  article-title: Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model
  publication-title: Landslides
  doi: 10.1007/s10346-017-0883-y
– volume: 34
  start-page: 241
  year: 2017
  ident: ref_149
  article-title: Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm
  publication-title: Eng. Comput.
  doi: 10.1007/s00366-017-0535-9
– volume: 14
  start-page: 447
  year: 2017
  ident: ref_73
  article-title: Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization
  publication-title: Landslides
  doi: 10.1007/s10346-016-0711-9
– volume: 74
  start-page: 1489
  year: 2014
  ident: ref_80
  article-title: Estimating landslide susceptibility through a artificial neural network classifier
  publication-title: Nat. Hazards
  doi: 10.1007/s11069-014-1245-x
– volume: 330
  start-page: 65
  year: 2018
  ident: ref_90
  article-title: Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping
  publication-title: Geoderma
  doi: 10.1016/j.geoderma.2018.05.027
– volume: 9
  start-page: 1053
  year: 2018
  ident: ref_156
  article-title: Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models
  publication-title: Geomat. Nat. Hazards Risk
  doi: 10.1080/19475705.2018.1481147
– volume: 14
  start-page: 2150
  year: 2017
  ident: ref_60
  article-title: Evaluating effectiveness of frequency ratio, fuzzy logic and logistic regression models in assessing landslide susceptibility: A case from Rudraprayag district, India
  publication-title: J. Mt. Sci.
  doi: 10.1007/s11629-017-4404-1
– volume: 261
  start-page: 431
  year: 2018
  ident: ref_144
  article-title: Prediction of sulfur solubility in supercritical sour gases using grey wolf optimizer-based support vector machine
  publication-title: J. Mol. Liq.
  doi: 10.1016/j.molliq.2018.04.070
– volume: 187
  start-page: 104396
  year: 2020
  ident: ref_157
  article-title: Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping
  publication-title: Catena
  doi: 10.1016/j.catena.2019.104396
– volume: 7
  start-page: 1835
  year: 2015
  ident: ref_4
  article-title: Evaluation of the influence of source and spatial resolution of DEMs on derivative products used in landslide mapping
  publication-title: Geomat. Nat. Hazards Risk
  doi: 10.1080/19475705.2015.1115431
– volume: 49
  start-page: 1363
  year: 2018
  ident: ref_78
  article-title: Rainfall-induced landslide susceptibility assessment using random forest weight at basin scale
  publication-title: Hydrol. Res.
  doi: 10.2166/nh.2017.044
– volume: 128
  start-page: 255
  year: 2017
  ident: ref_5
  article-title: Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: A comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods
  publication-title: Theor. Appl. Climatol.
  doi: 10.1007/s00704-015-1702-9
– ident: ref_84
  doi: 10.3390/app7101000
– volume: 22
  start-page: 941
  year: 2018
  ident: ref_65
  article-title: Prediction of Slope Stability using Naive Bayes Classifier
  publication-title: KSCE J. Civ. Eng.
  doi: 10.1007/s12205-018-1337-3
– volume: 183
  start-page: 104225
  year: 2019
  ident: ref_83
  article-title: Landslide susceptibility hazard map in southwest Sweden using artificial neural network
  publication-title: Catena
  doi: 10.1016/j.catena.2019.104225
– volume: 76
  start-page: 22
  year: 2017
  ident: ref_57
  article-title: Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran
  publication-title: Environ. Earth Sci.
  doi: 10.1007/s12665-017-6558-0
– volume: 115
  start-page: 55
  year: 2014
  ident: ref_153
  article-title: Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models
  publication-title: Catena
  doi: 10.1016/j.catena.2013.11.014
– volume: 9
  start-page: 485
  year: 2012
  ident: ref_120
  article-title: Rainfall thresholds for the forecasting of landslide occurrence at regional scale
  publication-title: Landslides
  doi: 10.1007/s10346-011-0308-2
– ident: ref_70
  doi: 10.3390/rs10101545
SSID ssj0000331904
Score 2.4150372
Snippet Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in...
SourceID doaj
proquest
crossref
SourceType Open Website
Aggregation Database
Enrichment Source
Index Database
StartPage 4966
SubjectTerms Algorithms
Artificial intelligence
Conditioning
Correlation analysis
Curvature
data collection
Datasets
Decision making
Disasters
Elevation
Fault lines
firefly algorithm
Geographic information systems
Geology
grey wolf optimizer algorithm
Heuristic methods
Human engineering
hybrid model
Hybrids
inventories
Land use planning
landslide susceptibility
Landslides
Landslides & mudslides
Lithology
Machine learning
Mapping
Natural disasters
Neural networks
Normalized difference vegetative index
Performance enhancement
prediction
Predictions
rain
Rainfall
regression analysis
Remote sensing
Sediment transport
Software
soil
Soil conditions
Spatial analysis
streams
Support vector machines
support vector regression algorithm
Topography
Training
Vegetation
SummonAdditionalLinks – databaseName: ProQuest Central
  dbid: BENPR
  link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZgC4ILj1LUhYKM4MIharp2Hj6hFnXbQ7WsKii9RRM_tiulSZtskcIP4Hcz4_XuHkBcOCaxEiszHn8ej7-PsQ-QKzMSqY0QHLtIGjWKlNR5hMvrZJQBImifzLk4yyaT_PJSTUPCrQtllauY6AO1aTTlyPeJ6ipPklEsPt3cRqQaRburQULjPtsipjI5YFtHx5Pp-TrLEgt0sVgueUkFdmC_7XD9JaXytIibmcgT9v8Rj_0kM376v917xp4EeMkPl_7wnN2z9TZ7FJTOr_pt9vDES_n2L9iv055Oa3W8cZy0PRGH8wufw-fndrYsj6055Wn5Cdqbf28qx79ghLme_7Qth9rwMcZLV_X8sJphXxZX1xwhMCeVY_RqPm1pE4gMT584ozPF1dxY_Fjna2l8WW6_w76Nj79-Po2CKkOkhZKLSMCBccrhUqbEH0p8-YAQBFEkAIKpEqd8cGlpjQPllIHM4qVROstK6UwCTrxkg7qp7S7jsUsTWWqVaaullgLyHKAEq-yBySCNh-zjykKFDpTlpJxRFbh0IWsWG2sO2ft125slUcdfWx2RodctiFzb32jaWRHGaiGBBJVKmRpKiiUWklIKBxlCt9zkZTJkeysfKMKI74qNAwzZu_VjHKu0AQO1be6ojUhzhAy5evXvV7xmj0dUO-PLZvbYYNHe2Tfsgf6xmHft2-DkvwHyqwmv
  priority: 102
  providerName: ProQuest
Title Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility
URI https://www.proquest.com/docview/2612855203
https://www.proquest.com/docview/2636803689
https://doaj.org/article/4aeb00b46d35405ea5b43fa73978d8b5
Volume 13
WOSCitedRecordID wos000737367900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  customDbUrl:
  eissn: 2072-4292
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000331904
  issn: 2072-4292
  databaseCode: DOA
  dateStart: 20090101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 2072-4292
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000331904
  issn: 2072-4292
  databaseCode: M~E
  dateStart: 20090101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
– providerCode: PRVPQU
  databaseName: Advanced Technologies & Aerospace Database
  customDbUrl:
  eissn: 2072-4292
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000331904
  issn: 2072-4292
  databaseCode: P5Z
  dateStart: 20090301
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/hightechjournals
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Earth, Atmospheric & Aquatic Science Database
  customDbUrl:
  eissn: 2072-4292
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000331904
  issn: 2072-4292
  databaseCode: PCBAR
  dateStart: 20090301
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/eaasdb
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Engineering Database
  customDbUrl:
  eissn: 2072-4292
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000331904
  issn: 2072-4292
  databaseCode: M7S
  dateStart: 20090301
  isFulltext: true
  titleUrlDefault: http://search.proquest.com
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 2072-4292
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000331904
  issn: 2072-4292
  databaseCode: BENPR
  dateStart: 20090301
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  customDbUrl:
  eissn: 2072-4292
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000331904
  issn: 2072-4292
  databaseCode: PIMPY
  dateStart: 20090301
  isFulltext: true
  titleUrlDefault: http://search.proquest.com/publiccontent
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Na9VAEF-kCnoRP_FpfazoxUPoa3aT7B5beY8K9Rmq1uolTPajfZAmkrwK8eDRv9uZTfpaUPDiJZBkYMPOZOc3s7O_YewVKG1jkboIwbGPpNVxpKVREYbXSZwBIuiQzDk-zJZLdXKi82utvqgmbKAHHiZuRwJ1tyllailDkThISik8ZOhHlVVlYC9F1HMtmAprsEDTmsmBj1TgwDtth3GXlDrQIV55oEDU_8c6HJzL4h67O6JCvjd8zX12w9UP2O2xQflZ_5D9OujpaFXHG8-pESeCZn4cEu78yJ0Otaw1p6Qqx6C_55-byvP3uBycr364lkNt-QIXN1_1fK86bVoUPOeIVzm1JEYT5HlLOzakJRrikA4AVyvrcLAuFL6EGtr-Efu0mH98cxCNLRQiI7RcRwJ2rdce444SZ4HI7QHxAkI-AEQ-Jfpn8GnprAfttYXM4a3VJstK6W0CXjxmW3VTuyeMz3yayNLozDgjjRSgFEAJTrtdm0E6m7DXl9NamJFfnNpcVAXGGaSC4koFE_ZyI_ttYNX4q9Q-aWcjQUzY4QHaRzHaR_Ev-5iw7UvdFuPv2RXEm6aSJJ6JCXuxeY0_Fu2WQO2aC5IRqUL_rvTT__Edz9idmMphQiXMNttatxfuObtlvq9XXTtlN_fny_xoGqx4SgWoH-j6c47XPPmK7_O37_IvvwEynv3j
linkProvider Directory of Open Access Journals
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwELXKFlQufBQQCwWMgAOHqGnsJPYBofKx3VW3ywqVtpyCE9vbldKkJFtQ-AH8HH4jM97s7gHErQeOSSxHSV5m3ozH8wh5roTUAYuMB-TYelzLwJM8Ex6E12EQK2DQLplzNIxHI3FyIsdr5NdiLwyWVS5sojPUuswwR76Nra5EGAY-e33-1UPVKFxdXUhozGGxb5rvELLVrwbv4Pu-CILe-8O3fa9VFfAyJvnMY2pHW2mBiqeMSez3rsCFAgtSCshACi5L2Sg12ipppVaxgUMtszhOudWhsgzmvULWOYBddMj6eHAw_rzM6vgMIO3zeR9UmN7frmqI9ziXrg3jyvM5gYA_7L9zar2b_9vruEVutPSZ7s7xfpusmWKTbLRK7qfNJrm256SKmzvkZ7_B3Wg1LS1F7VKIM-iRW6OgH81kXv5bUMxD0z3AMz0uc0s_gAU9m_4wFVWFpj3wBzZv6G4-gWefnZ5RoPgUVZzhr6XjChe5ENh4iyHumc6n2sDNalcr5MqOm7vk06W8kHukU5SFuU-ob6OQp5mMM5PxjDMlhFKpMtLs6FhFfpe8XCAiydqW7KgMkicQmiF6khV6uuTZcuz5vBHJX0e9QWAtR2DzcHeirCZJa4sSrlAwKuWRxqRfaFSYcmZVDNRUaJGGXbK1wFzSWrQ6WQGuS54uL4MtwgUmVZjyAsewSAAlEvLBv6d4Qjb6hwfDZDgY7T8k1wOsE3IlQlukM6suzCNyNfs2m9bV4_YHo-TLZYP4N978aZM
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nc9MwENWUlK8LHwWGQAExwIGDJ64l29KBYdKWtJ1mQqYDbW9GtqQ0M65d7BTG_AB-FL-OXcVJDjDceuAYRyOP7afdt6vVPkJeKyF1wCLjATm2Htcy8CTPhAfhdRjEChi0S-YcD-PRSJyeyvEa-bU4C4NllQub6Ay1LjPMkfew1ZUIw8BnPduWRYx3B-8vvnqoIIU7rQs5jTlEDk3zHcK3-t3BLnzrN0Ew-PBpZ99rFQa8jEk-85ja0lZaoOUpYxJ7vytwp8CIlAJikIL7UjZKjbZKWqlVbOCnllkcp9zqUFkG814j6yKK_aBD1sc72_2jZYbHZwBvn897osL0fq-qIfbjXLqWjCsv6MQC_vAFzsEN7v7Pr-YeudPSatqfr4P7ZM0UG-RWq_B-1myQG3tOwrh5QH7uN3hKraalpahpCvEHPXZ7F_TITOZlwQXF_DTdA5zTkzK39CNY1vPpD1NRVWg6AD9h84b28wk8--zsnAL1p6juDKuZjivc_ELA4y2GeJY6n2oDN6tdDZErR24eks9X8kIekU5RFuYxob6NQp5mMs5MxjPOlBBKpcpIs6VjFfld8naBjiRrW7WjYkieQMiGSEpWSOqSV8uxF_MGJX8dtY0gW47ApuLuQllNktZGJVyhkFTKI43JwNCoMOXMqhgoq9AiDbtkc4G_pLV0dbICX5e8XP4NNgo3nlRhykscwyIBVEnIJ_-e4gW5CchNhgejw6fkdoDlQ65yaJN0ZtWleUauZ99m07p63q41Sr5cNYZ_A_AXcgM
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Hybrids+of+Support+Vector+Regression+with+Grey+Wolf+Optimizer+and+Firefly+Algorithm+for+Spatial+Prediction+of+Landslide+Susceptibility&rft.jtitle=Remote+sensing+%28Basel%2C+Switzerland%29&rft.au=Ru+Liu&rft.au=Jianbing+Peng&rft.au=Yanqiu+Leng&rft.au=Saro+Lee&rft.date=2021-12-01&rft.pub=MDPI+AG&rft.eissn=2072-4292&rft.volume=13&rft.issue=24&rft.spage=4966&rft_id=info:doi/10.3390%2Frs13244966&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_4aeb00b46d35405ea5b43fa73978d8b5
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-4292&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-4292&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-4292&client=summon