Matches in SemOpenAlex for { <https://semopenalex.org/work/W3091524508> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W3091524508 endingPage "9" @default.
- W3091524508 startingPage "1" @default.
- W3091524508 abstract "Precipitation nowcasting plays a key role in land security and emergency management of natural calamities. A majority of existing deep learning-based techniques realize precipitation nowcasting by learning a deep nonlinear function from a single information source, e.g., weather radar. In this study, we propose a novel multimodal semisupervised deep graph learning framework for precipitation nowcasting. Unlike existing studies, different modalities of observation data (including both meteorological and nonmeteorological data) are modeled jointly, thereby benefiting each other. All information is converted into image structures, next, precipitation nowcasting is deemed as a computer vision task to be optimized. To handle areas with unavailable precipitation, we convert all observation information into a graph structure and introduce a semisupervised graph convolutional network with a sequence connect architecture to learn the features of all local areas. With the learned features, precipitation is predicted through a multilayer fully connected regression network. Experiments on real datasets confirm the effectiveness of the proposed method." @default.
- W3091524508 created "2020-10-08" @default.
- W3091524508 creator A5013798181 @default.
- W3091524508 creator A5022675701 @default.
- W3091524508 creator A5033009526 @default.
- W3091524508 creator A5044936528 @default.
- W3091524508 creator A5072175031 @default.
- W3091524508 creator A5074252142 @default.
- W3091524508 creator A5076127071 @default.
- W3091524508 date "2020-10-01" @default.
- W3091524508 modified "2023-10-16" @default.
- W3091524508 title "Multimodal Semisupervised Deep Graph Learning for Automatic Precipitation Nowcasting" @default.
- W3091524508 cites W2064675550 @default.
- W3091524508 cites W2191380711 @default.
- W3091524508 cites W2407712691 @default.
- W3091524508 cites W2594928006 @default.
- W3091524508 cites W2919115771 @default.
- W3091524508 cites W2943446701 @default.
- W3091524508 cites W2972792628 @default.
- W3091524508 cites W2978043874 @default.
- W3091524508 doi "https://doi.org/10.1155/2020/4018042" @default.
- W3091524508 hasPublicationYear "2020" @default.
- W3091524508 type Work @default.
- W3091524508 sameAs 3091524508 @default.
- W3091524508 citedByCount "5" @default.
- W3091524508 countsByYear W30915245082021 @default.
- W3091524508 countsByYear W30915245082022 @default.
- W3091524508 countsByYear W30915245082023 @default.
- W3091524508 crossrefType "journal-article" @default.
- W3091524508 hasAuthorship W3091524508A5013798181 @default.
- W3091524508 hasAuthorship W3091524508A5022675701 @default.
- W3091524508 hasAuthorship W3091524508A5033009526 @default.
- W3091524508 hasAuthorship W3091524508A5044936528 @default.
- W3091524508 hasAuthorship W3091524508A5072175031 @default.
- W3091524508 hasAuthorship W3091524508A5074252142 @default.
- W3091524508 hasAuthorship W3091524508A5076127071 @default.
- W3091524508 hasBestOaLocation W30915245081 @default.
- W3091524508 hasConcept C107054158 @default.
- W3091524508 hasConcept C108583219 @default.
- W3091524508 hasConcept C119857082 @default.
- W3091524508 hasConcept C124101348 @default.
- W3091524508 hasConcept C132525143 @default.
- W3091524508 hasConcept C153180895 @default.
- W3091524508 hasConcept C153294291 @default.
- W3091524508 hasConcept C154945302 @default.
- W3091524508 hasConcept C205649164 @default.
- W3091524508 hasConcept C2781013037 @default.
- W3091524508 hasConcept C41008148 @default.
- W3091524508 hasConcept C554190296 @default.
- W3091524508 hasConcept C76155785 @default.
- W3091524508 hasConcept C80444323 @default.
- W3091524508 hasConcept C81363708 @default.
- W3091524508 hasConceptScore W3091524508C107054158 @default.
- W3091524508 hasConceptScore W3091524508C108583219 @default.
- W3091524508 hasConceptScore W3091524508C119857082 @default.
- W3091524508 hasConceptScore W3091524508C124101348 @default.
- W3091524508 hasConceptScore W3091524508C132525143 @default.
- W3091524508 hasConceptScore W3091524508C153180895 @default.
- W3091524508 hasConceptScore W3091524508C153294291 @default.
- W3091524508 hasConceptScore W3091524508C154945302 @default.
- W3091524508 hasConceptScore W3091524508C205649164 @default.
- W3091524508 hasConceptScore W3091524508C2781013037 @default.
- W3091524508 hasConceptScore W3091524508C41008148 @default.
- W3091524508 hasConceptScore W3091524508C554190296 @default.
- W3091524508 hasConceptScore W3091524508C76155785 @default.
- W3091524508 hasConceptScore W3091524508C80444323 @default.
- W3091524508 hasConceptScore W3091524508C81363708 @default.
- W3091524508 hasFunder F4320334897 @default.
- W3091524508 hasLocation W30915245081 @default.
- W3091524508 hasOpenAccess W3091524508 @default.
- W3091524508 hasPrimaryLocation W30915245081 @default.
- W3091524508 hasRelatedWork W2337926734 @default.
- W3091524508 hasRelatedWork W2732542196 @default.
- W3091524508 hasRelatedWork W2738221750 @default.
- W3091524508 hasRelatedWork W3156786002 @default.
- W3091524508 hasRelatedWork W4311257506 @default.
- W3091524508 hasRelatedWork W4312417841 @default.
- W3091524508 hasRelatedWork W4320802194 @default.
- W3091524508 hasRelatedWork W4321369474 @default.
- W3091524508 hasRelatedWork W4366224123 @default.
- W3091524508 hasRelatedWork W564581980 @default.
- W3091524508 hasVolume "2020" @default.
- W3091524508 isParatext "false" @default.
- W3091524508 isRetracted "false" @default.
- W3091524508 magId "3091524508" @default.
- W3091524508 workType "article" @default.