FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3206564585> ?p ?o ?g. }

• W3206564585 abstract "Abstract In the present research, Indian Ocean Dipole (IOD) prediction was explored using statistical methods based on deep learning techniques. First, convolutional neural network (CNN) models were trained using sea‐surface temperature anomaly (SSTA) maps of the Indian Ocean from 1854 to 1989, and the properly trained CNN models were then validated with the period from 1991 to 2019. The results indicate that the deep learning approach is capable of forecasting the IOD at lead times up to 7 months. The forecast skills of CNN are superior to those of the dynamic models in the North American Multi‐Model Ensemble (NMME). The CNN outperforms the NMME with lower sensitivity to predictability barriers and fewer systematic errors. Moreover, the gradient heat map analysis demonstrates that the triggering precursors selected by CNN models for IOD events are novel and physically sensible. These results suggest the CNN to be a new and effective tool for both IOD prediction and comprehension." @default.
• W3206564585 created "2021-10-25" @default.
• W3206564585 creator A5007719342 @default.
• W3206564585 creator A5022810915 @default.
• W3206564585 creator A5030863883 @default.
• W3206564585 creator A5062620742 @default.
• W3206564585 creator A5079272207 @default.
• W3206564585 creator A5087585615 @default.
• W3206564585 date "2021-10-22" @default.
• W3206564585 modified "2023-10-16" @default.
• W3206564585 title "Forecasting the Indian Ocean Dipole With Deep Learning Techniques" @default.
• W3206564585 cites W1849277567 @default.
• W3206564585 cites W1949400074 @default.
• W3206564585 cites W1966216504 @default.
• W3206564585 cites W1968711501 @default.
• W3206564585 cites W1971337131 @default.
• W3206564585 cites W1976791901 @default.
• W3206564585 cites W1976873458 @default.
• W3206564585 cites W1977363963 @default.
• W3206564585 cites W1982974524 @default.
• W3206564585 cites W1985669309 @default.
• W3206564585 cites W1987398183 @default.
• W3206564585 cites W1999923532 @default.
• W3206564585 cites W2006572356 @default.
• W3206564585 cites W2018270410 @default.
• W3206564585 cites W2022280600 @default.
• W3206564585 cites W2023560724 @default.
• W3206564585 cites W2030116498 @default.
• W3206564585 cites W2037362466 @default.
• W3206564585 cites W2041926769 @default.
• W3206564585 cites W2051228670 @default.
• W3206564585 cites W2056116574 @default.
• W3206564585 cites W2065018036 @default.
• W3206564585 cites W2077315026 @default.
• W3206564585 cites W2077518181 @default.
• W3206564585 cites W2082001988 @default.
• W3206564585 cites W2084279619 @default.
• W3206564585 cites W2091053508 @default.
• W3206564585 cites W2123217957 @default.
• W3206564585 cites W2124037634 @default.
• W3206564585 cites W2133351779 @default.
• W3206564585 cites W2141216009 @default.
• W3206564585 cites W2175236913 @default.
• W3206564585 cites W2418056382 @default.
• W3206564585 cites W2572967643 @default.
• W3206564585 cites W2592898963 @default.
• W3206564585 cites W2738516213 @default.
• W3206564585 cites W2739353279 @default.
• W3206564585 cites W2739993423 @default.
• W3206564585 cites W2788970460 @default.
• W3206564585 cites W2898030472 @default.
• W3206564585 cites W2901562893 @default.
• W3206564585 cites W2919115771 @default.
• W3206564585 cites W2920970074 @default.
• W3206564585 cites W2967733281 @default.
• W3206564585 cites W2973731563 @default.
• W3206564585 cites W2974527409 @default.
• W3206564585 cites W2998978152 @default.
• W3206564585 cites W2999233839 @default.
• W3206564585 cites W3003979916 @default.
• W3206564585 cites W3014071393 @default.
• W3206564585 cites W3028525138 @default.
• W3206564585 cites W3040706550 @default.
• W3206564585 cites W3043233558 @default.
• W3206564585 cites W3102564565 @default.
• W3206564585 doi "https://doi.org/10.1029/2021gl094407" @default.
• W3206564585 hasPublicationYear "2021" @default.
• W3206564585 type Work @default.
• W3206564585 sameAs 3206564585 @default.
• W3206564585 citedByCount "15" @default.
• W3206564585 countsByYear W32065645852021 @default.
• W3206564585 countsByYear W32065645852022 @default.
• W3206564585 countsByYear W32065645852023 @default.
• W3206564585 crossrefType "journal-article" @default.
• W3206564585 hasAuthorship W3206564585A5007719342 @default.
• W3206564585 hasAuthorship W3206564585A5022810915 @default.
• W3206564585 hasAuthorship W3206564585A5030863883 @default.
• W3206564585 hasAuthorship W3206564585A5062620742 @default.
• W3206564585 hasAuthorship W3206564585A5079272207 @default.
• W3206564585 hasAuthorship W3206564585A5087585615 @default.
• W3206564585 hasConcept C105795698 @default.
• W3206564585 hasConcept C108583219 @default.
• W3206564585 hasConcept C119857082 @default.
• W3206564585 hasConcept C121332964 @default.
• W3206564585 hasConcept C127313418 @default.
• W3206564585 hasConcept C12997251 @default.
• W3206564585 hasConcept C134097258 @default.
• W3206564585 hasConcept C154945302 @default.
• W3206564585 hasConcept C197640229 @default.
• W3206564585 hasConcept C26873012 @default.
• W3206564585 hasConcept C33923547 @default.
• W3206564585 hasConcept C41008148 @default.
• W3206564585 hasConcept C49204034 @default.
• W3206564585 hasConcept C62897925 @default.
• W3206564585 hasConcept C81363708 @default.
• W3206564585 hasConceptScore W3206564585C105795698 @default.
• W3206564585 hasConceptScore W3206564585C108583219 @default.
• W3206564585 hasConceptScore W3206564585C119857082 @default.
• W3206564585 hasConceptScore W3206564585C121332964 @default.
• W3206564585 hasConceptScore W3206564585C127313418 @default.

• next