Vote for your favorite mineral in #MinCup25! - Paddlewheelite vs. Mannardite
It's a battle of the tiny as bright green #paddlewheelite goes up against jet black #mannardite. Both are small yet mighty minerals, but only one can win!
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Sahin, Emrehan Kutlug (2023) Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost. Stochastic Environmental Research and Risk Assessment, 37 (3) 1067-1092 doi:10.1007/s00477-022-02330-y

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleImplementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost
JournalStochastic Environmental Research and Risk Assessment
AuthorsSahin, Emrehan KutlugAuthor
Year2023 (March)Volume37
Issue3
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1007/s00477-022-02330-ySearch in ResearchGate
Generate Citation Formats
Mindat Ref. ID15728131Long-form Identifiermindat:1:5:15728131:8
GUID0
Full ReferenceSahin, Emrehan Kutlug (2023) Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost. Stochastic Environmental Research and Risk Assessment, 37 (3) 1067-1092 doi:10.1007/s00477-022-02330-y
Plain TextSahin, Emrehan Kutlug (2023) Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost. Stochastic Environmental Research and Risk Assessment, 37 (3) 1067-1092 doi:10.1007/s00477-022-02330-y
In(2023, March) Stochastic Environmental Research and Risk Assessment Vol. 37 (3) Springer Science and Business Media LLC

See Also

These are possibly similar items as determined by title/reference text matching only.

Moayedi, Hossein, Khasmakhi, Mohammad Ali Salehi Amin (2023) Wildfire susceptibility mapping using two empowered machine learning algorithms. Stochastic Environmental Research and Risk Assessment, 37 (1) 49-72 doi:10.1007/s00477-022-02273-4
Demir, Selçuk, Sahin, Emrehan Kutlug (2024) The effectiveness of data pre-processing methods on the performance of machine learning techniques using RF, SVR, Cubist and SGB: a study on undrained shear strength prediction. Stochastic Environmental Research and Risk Assessment, 38 (8) 3273-3290 doi:10.1007/s00477-024-02745-9
IBAN, Muzaffer Can, BILGILIOGLU, Suleyman Sefa (2023) Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach. Stochastic Environmental Research and Risk Assessment, 37 (6) 2243-2270 doi:10.1007/s00477-023-02392-6
Sahin, Emrehan Kutlug, Ipbuker, Cengizhan, Kavzoglu, Taskin (2015) A Comparison of Feature and Expert-based Weighting Algorithms in Landslide Susceptibility Mapping. Procedia Earth and Planetary Science, 15. 462-467 doi:10.1016/j.proeps.2015.08.043
Demir, Selçuk, Şahin, Emrehan Kutluğ (2022) Liquefaction prediction with robust machine learning algorithms (SVM, RF, and XGBoost) supported by genetic algorithm-based feature selection and parameter optimization from the perspective of data processing. Environmental Earth Sciences, 81 (18) doi:10.1007/s12665-022-10578-4
Kundu, Souvik; Mondal, Tarun Kumar (2025) Flood susceptibility mapping in Indian Sundarban delta using multivariate statistics and machine learning algorithms in GIS. Stochastic Environmental Research and Risk Assessment, 39 (5). doi:10.1007/s00477-025-02957-7
Liu, Chao (2025) Landslide susceptibility mapping using CNN models based on factor visualization and transfer learning. Stochastic Environmental Research and Risk Assessment, 39 (1). doi:10.1007/s00477-024-02859-0
Hamedi, Hossein, Alesheikh, Ali Asghar, Panahi, Mahdi, Lee, Saro (2022) Landslide susceptibility mapping using deep learning models in Ardabil province, Iran. Stochastic Environmental Research and Risk Assessment, 36 (12) 4287-4310 doi:10.1007/s00477-022-02263-6
Razavi-Termeh, Seyed Vahid, Sadeghi-Niaraki, Abolghasem, Naqvi, Rizwan Ali, Choi, Soo-Mi (2025) Investigating the efficacy of physics-based metaheuristic algorithms in combination with explainable ensemble machine-learning models for landslide susceptibility mapping. Stochastic Environmental Research and Risk Assessment, 39 (3). doi:10.1007/s00477-025-02911-7
Kavzoglu, Taskin, Kutlug Sahin, Emrehan, Colkesen, Ismail (2015) Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Engineering Geology, 192. 101-112 doi:10.1016/j.enggeo.2015.04.004
Kavzoglu, Taskin, Kutlug Sahin, Emrehan, Colkesen, Ismail (2015) An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district. Natural Hazards, 76. 471-496 doi:10.1007/s11069-014-1506-8
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: September 16, 2025 19:11:52
Go to top of page