Vote for your favorite mineral in #MinCup25! - Stibnite vs. Okenite
It's a battle of dark and light as soft, dramatic stibnite goes up against adorable cottonballs of Okenite.
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

Lian, Cheng, Zeng, Zhigang, Yao, Wei, Tang, Huiming (2014) Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level. Stochastic Environmental Research and Risk Assessment, 28 (8). 1957-1972 doi:10.1007/s00477-014-0875-6

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleExtreme learning machine for the displacement prediction of landslide under rainfall and reservoir level
JournalStochastic Environmental Research and Risk Assessment
AuthorsLian, ChengAuthor
Zeng, ZhigangAuthor
Yao, WeiAuthor
Tang, HuimingAuthor
Year2014 (December)Volume28
Issue8
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1007/s00477-014-0875-6Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9339930Long-form Identifiermindat:1:5:9339930:8
GUID0
Full ReferenceLian, Cheng, Zeng, Zhigang, Yao, Wei, Tang, Huiming (2014) Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level. Stochastic Environmental Research and Risk Assessment, 28 (8). 1957-1972 doi:10.1007/s00477-014-0875-6
Plain TextLian, Cheng, Zeng, Zhigang, Yao, Wei, Tang, Huiming (2014) Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level. Stochastic Environmental Research and Risk Assessment, 28 (8). 1957-1972 doi:10.1007/s00477-014-0875-6
In(2014, December) Stochastic Environmental Research and Risk Assessment Vol. 28 (8) Springer Science and Business Media LLC

See Also

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

Lian, Cheng, Zeng, Zhigang, Yao, Wei, Tang, Huiming (2013) Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine. Natural Hazards, 66. 759-771 doi:10.1007/s11069-012-0517-6
Yao, Wei, Zeng, Zhigang, Lian, Cheng, Tang, Huiming (2015) Training enhanced reservoir computing predictor for landslide displacement. Engineering Geology, 188. 101-109 doi:10.1016/j.enggeo.2014.11.008
Lian, Cheng, Zeng, Zhigang, Yao, Wei, Tang, Huiming (2015) Multiple neural networks switched prediction for landslide displacement. Engineering Geology, 186. 91-99 doi:10.1016/j.enggeo.2014.11.014
Cantet, Philippe, Arnaud, Patrick (2014) Extreme rainfall analysis by a stochastic model: impact of the copula choice on the sub-daily rainfall generation. Stochastic Environmental Research and Risk Assessment, 28 (6). 1479-1492 doi:10.1007/s00477-014-0852-0
Li, Changdong, Tang, Huiming, Ge, Yunfeng, Hu, Xinli, Wang, Liangqing (2014) Application of back-propagation neural network on bank destruction forecasting for accumulative landslides in the three Gorges Reservoir Region, China. Stochastic Environmental Research and Risk Assessment, 28 (6). 1465-1477 doi:10.1007/s00477-014-0848-9
Jones, Owen D., Nyman, Petter, Sheridan, Gary J. (2014) Modelling the effects of fire and rainfall regimes on extreme erosion events in forested landscapes. Stochastic Environmental Research and Risk Assessment, 28 (8). 2015-2025 doi:10.1007/s00477-014-0891-6
Zou, Zongxing, Yang, Yingming, Fan, Zhiqiang, Tang, Huiming, Zou, Meng, Hu, Xinli, Xiong, Chengren, Ma, Junwei (2020) Suitability of data preprocessing methods for landslide displacement forecasting. Stochastic Environmental Research and Risk Assessment, 34 (8) 1105-1119 doi:10.1007/s00477-020-01824-x
Fu, Zhiyong, Long, Jingjing, Chen, Wenqiang, Li, Changdong, Zhang, Haikuan, Yao, Wenmin (2021) Reliability of the prediction model for landslide displacement with step-like behavior. Stochastic Environmental Research and Risk Assessment, 35 (11) 2335-2353 doi:10.1007/s00477-021-02029-6
Vidal, Ignacio (2014) A Bayesian analysis of the Gumbel distribution: an application to extreme rainfall data. Stochastic Environmental Research and Risk Assessment, 28 (3). 571-582 doi:10.1007/s00477-013-0773-3
Chou, Chien-Ming (2014) Complexity analysis of rainfall and runoff time series based on sample entropy in different temporal scales. Stochastic Environmental Research and Risk Assessment, 28 (6). 1401-1408 doi:10.1007/s00477-014-0859-6
Löwe, Roland, Mikkelsen, Peter Steen, Madsen, Henrik (2014) Stochastic rainfall-runoff forecasting: parameter estimation, multi-step prediction, and evaluation of overflow risk. Stochastic Environmental Research and Risk Assessment, 28 (3). 505-516 doi:10.1007/s00477-013-0768-0
 
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 2, 2025 19:03:24
Go to top of page