Vote for your favorite mineral in #MinCup25! - Carpathite vs. Leucite
Brace for oddness in a match between one of the very few hydrocarbon minerals carpathite and the temperature-flipping mineral leucite.
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

Rahmati, Omid, Tahmasebipour, Nasser, Haghizadeh, Ali, Pourghasemi, Hamid Reza, Feizizadeh, Bakhtiar (2017) Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology, 298. 118-137 doi:10.1016/j.geomorph.2017.09.006

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleEvaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion
JournalGeomorphology
AuthorsRahmati, OmidAuthor
Tahmasebipour, NasserAuthor
Haghizadeh, AliAuthor
Pourghasemi, Hamid RezaAuthor
Feizizadeh, BakhtiarAuthor
Year2017 (December)Volume298
PublisherElsevier BV
DOIdoi:10.1016/j.geomorph.2017.09.006Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID949125Long-form Identifiermindat:1:5:949125:4
GUID0
Full ReferenceRahmati, Omid, Tahmasebipour, Nasser, Haghizadeh, Ali, Pourghasemi, Hamid Reza, Feizizadeh, Bakhtiar (2017) Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology, 298. 118-137 doi:10.1016/j.geomorph.2017.09.006
Plain TextRahmati, Omid, Tahmasebipour, Nasser, Haghizadeh, Ali, Pourghasemi, Hamid Reza, Feizizadeh, Bakhtiar (2017) Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology, 298. 118-137 doi:10.1016/j.geomorph.2017.09.006
In(2017) Geomorphology Vol. 298. Elsevier BV

See Also

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

Rahmati, Omid, Tahmasebipour, Naser, Haghizadeh, Ali, Pourghasemi, Hamid Reza, Feizizadeh, Bakhtiar (2017) Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework. Science of The Total Environment, 579. 913-927 doi:10.1016/j.scitotenv.2016.10.176
Rahmati, Omid, Haghizadeh, Ali, Pourghasemi, Hamid Reza, Noormohamadi, Farhad (2016) Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison. Natural Hazards, 82. 1231-1258 doi:10.1007/s11069-016-2239-7
Moghaddam, Davoud Davoudi, Rahmati, Omid, Panahi, Mahdi, Tiefenbacher, John, Darabi, Hamid, Haghizadeh, Ali, Haghighi, Ali Torabi, Nalivan, Omid Asadi, Tien Bui, Dieu (2020) The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers. CATENA, 187. 104421 doi:10.1016/j.catena.2019.104421
Garosi, Younes, Sheklabadi, Mohsen, Conoscenti, Christian, Pourghasemi, Hamid Reza, Van Oost, Kristof (2019) Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Science of The Total Environment, 664. 1117-1132 doi:10.1016/j.scitotenv.2019.02.093
Gayen, Amiya, Pourghasemi, Hamid Reza, Saha, Sunil, Keesstra, Saskia, Bai, Shibiao (2019) Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Science of The Total Environment, 668. 124-138 doi:10.1016/j.scitotenv.2019.02.436
Pourghasemi, Hamid Reza, Rahmati, Omid (2018) Prediction of the landslide susceptibility: Which algorithm, which precision?. CATENA, 162. 177-192 doi:10.1016/j.catena.2017.11.022
Soleimanpour, Seyed Masoud, Pourghasemi, Hamid Reza, Zare, Maryam (2021) A comparative assessment of gully erosion spatial predictive modeling using statistical and machine learning models. CATENA, 207. 105679 doi:10.1016/j.catena.2021.105679
Youssef, Ahmed M., Mahdi, Ali M., Al-Katheri, Mohamed M., Pouyan, Soheila, Pourghasemi, Hamid Reza (2023) Multi-hazards (landslides, floods, and gully erosion) modeling and mapping using machine learning algorithms. Journal of African Earth Sciences, 197. 104788 doi:10.1016/j.jafrearsci.2022.104788
Rahmati, Omid, Pourghasemi, Hamid Reza, Melesse, Assefa M. (2016) Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran. CATENA, 137. 360-372 doi:10.1016/j.catena.2015.10.010
Garosi, Younes, Sheklabadi, Mohsen, Pourghasemi, Hamid Reza, Besalatpour, Ali Asghar, Conoscenti, Christian, Van Oost, Kristof (2018) Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping. Geoderma, 330. 65-78 doi:10.1016/j.geoderma.2018.05.027
Amiri, Mahdis, Pourghasemi, Hamid Reza, Ghanbarian, Gholam Abbas, Afzali, Sayed Fakhreddin (2019) Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms. Geoderma, 340. 55-69 doi:10.1016/j.geoderma.2018.12.042
 
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 Β© 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 6, 2025 16:18:39
Go to top of page