???ENUM_LANGUAGE_JA???
 
???mainMenu_lnkPrivacyPolicy??? ???mainMenu_lnkPolicy???

???ViewItemPage???

???ViewItemFull_lblItemActions??????List_lblExportOptions???
 
 
???export_btDownload??????export_btEMail???
 ???ViewItemFull_lblSubHeaderLocalTags??????ViewItemFull_btnItemVersions??????ViewItemFull_btnItemView??????ViewItemOverview_lblLinkOverviewPage???
  PSLSA v2.0: An automatic Python package integrating machine learning models for regional landslide susceptibility assessment

Guo, Z., Wang, H., He, J., Huang, D., Song, Y., Wang, T., Liu, Y., Ferrer, J. V. (2025): PSLSA v2.0: An automatic Python package integrating machine learning models for regional landslide susceptibility assessment. - Environmental Modelling and Software, 186, 106367.
https://doi.org/10.1016/j.envsoft.2025.106367

Item is

 

???ViewItemFull_lblShowGroup??? ???ViewItemFull_lblAll??? ???ViewItemFull_lblHideGroup??? ???ViewItemFull_lblAll???

???ViewItemFull_lblBasic???

???ViewItemFull_lblShowGroup??? ???ViewItemFull_lblHideGroup???
???ViewItemFull_lblCiteItemAs???: https://publications.pik-potsdam.de/pubman/item/item_33123 ???ViewItemFull_lblCiteItemVersionAs???: https://publications.pik-potsdam.de/pubman/item/item_33123_2
???ViewItemFull_lblGenre???: ???ENUM_GENRE_ARTICLE???

???ViewItemMedium_lblSubHeaderFile???

???ViewItemFull_lblShowGroup??? ???ViewItemMedium_lblSubHeaderFile???
???ViewItemFull_lblHideGroup??? ???ViewItemMedium_lblSubHeaderFile???
:
Guo_2025_1-s2.0-S1364815225000519-main.pdf (???ENUM_CONTENTCATEGORY_publisher-version???), 21???ViewItemMedium_lblFileSizeMB???
 
???ViewItemFull_lblCiteFileAs???:
???lbl_noEntry???
???ViewItemMedium_lblFileName???:
Guo_2025_1-s2.0-S1364815225000519-main.pdf
???ViewItemMedium_lblFileDescription???:
???lbl_noEntry???
???ViewItemMedium_lblFileOaSatus???:
???ViewItemMedium_lblFileVisibility???:
???ENUM_VISIBILITY_PRIVATE???
???ViewItemFull_lblFileMimeTypeSize???:
application/pdf
???ViewItemFull_lblTechnicalMetadata???:
???ViewItem_lblCopyrightDate???:
???lbl_noEntry???
???ViewItem_lblCopyrightInfo???:
???lbl_noEntry???
???ViewItemFull_lblFileLicense???:
???lbl_noEntry???

???ViewItemFull_lblSubHeaderLocators???

???ViewItemFull_lblShowGroup???

???ViewItemFull_lblCreators???

???ViewItemFull_lblShowGroup???
???ViewItemFull_lblHideGroup???
 ???ViewItemFull_lblCreators???:
Guo, Zizheng1, ???ENUM_CREATORROLE_AUTHOR???
Wang, Haojie1, ???ENUM_CREATORROLE_AUTHOR???
He, Jun1, ???ENUM_CREATORROLE_AUTHOR???
Huang, Da1, ???ENUM_CREATORROLE_AUTHOR???
Song, Yixiang1, ???ENUM_CREATORROLE_AUTHOR???
Wang, Tengfei1, ???ENUM_CREATORROLE_AUTHOR???
Liu, Yuanbo1, ???ENUM_CREATORROLE_AUTHOR???
Ferrer, Joaquin Vicente2, ???ENUM_CREATORROLE_AUTHOR???           
???ViewItemFull_lblAffiliations???:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

???EditItem_lblContent???

???ViewItemFull_lblShowGroup???
???ViewItemFull_lblHideGroup???
???ViewItemFull_lblSubject???: ???lbl_noEntry???
 ???ViewItemFull_lblAbstract???: Accurate landslide susceptibility assessments (LSA) are crucial for civil protection and land use planning. This study introduces PSLSA v2.0 as an open-source Python package that can conduct LSA automatically. It integrates six sophisticated machine learning algorithms (C5.0, SVM, LR, RF, MLP, XGBoost), and allows arbitrary combinations of influencing factors to generate landslide susceptibility index (LSI). We demonstrate how factor contribution and hyperparameter optimization as additional outputs can enhance the model interpretability. We apply PSLSA to a case study focused from Linzhi City in the Tibetan Plateau of China, that has undergone significant engineering modifications on its slopes. The results reveal that slope and aspect are the dominant factors in determining landslide susceptibility. All the six algorithms have an accuracy of over 80%. Although the distribution patterns of LSI vary, the C5.0 model is set apart with the best performance. PSLSA provides a powerful tool for stakeholders especially the non-geohazard professionals.

???ViewItemFull_lblSubHeaderDetails???

???ViewItemFull_lblShowGroup???
???ViewItemFull_lblHideGroup???
???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2025-02-112025-03-01
 ???ViewItemFull_lblPublicationStatus???: ???ViewItem_lblPublicationState_published-in-print???
 ???ViewItemFull_lblPages???: ???lbl_noEntry???
 ???ViewItemFull_lblPublishingInfo???: ???lbl_noEntry???
 ???ViewItemFull_lblTOC???: ???lbl_noEntry???
 ???ViewItemFull_lblRevisionMethod???: ???ENUM_REVIEWMETHOD_PEER???
 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1016/j.envsoft.2025.106367
???ENUM_IDENTIFIERTYPE_MDB_ID???: No data to archive
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD4 - Complexity Science
 ???ViewItemFull_lblDegreeType???: ???lbl_noEntry???

???ViewItemFull_lblSubHeaderEvent???

???ViewItemFull_lblShowGroup???

???ViewItemFull_lblSubHeaderLegalCase???

???ViewItemFull_lblShowGroup???

???g_project_info???

???ViewItemFull_lblShowGroup???

???ViewItemFull_lblSubHeaderSource??? 1

???ViewItemFull_lblShowGroup???
???ViewItemFull_lblHideGroup???
???ViewItemFull_lblSourceTitle???: Environmental Modelling and Software
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus, p3
 ???ViewItemFull_lblSourceCreators???:
???ViewItemFull_lblSourceAffiliations???:
???ViewItemFull_lblSourcePubInfo???: ???lbl_noEntry???
???ViewItemFull_lblPages???: ???lbl_noEntry??? ???ViewItemFull_lblSourceVolumeIssue???: 186 ???ViewItemFull_lblSourceSequenceNo???: 106367 ???ViewItemFull_lblSourceStartEndPage???: ???lbl_noEntry??? ???ViewItemFull_lblSourceIdentifier???: ???ENUM_IDENTIFIERTYPE_CONE???: https://publications.pik-potsdam.de/cone/journals/resource/journals127
???ENUM_IDENTIFIERTYPE_PUBLISHER???: Elsevier