Matches in SemOpenAlex for { <https://semopenalex.org/work/W4316924467> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W4316924467 abstract "Rain that falls at a small and moderate rate is a blessing but incessant rainfall can bring many adverse effects such as loss of life, and destruction of property, crops and fields. One of the ways that can reduce the negative effects of natural disasters like this is to study trends and make predictions about what will happen. The predictive system, e.g. a nowcasting model can play an important role in dealing with rain issues, especially being able to provide early warning before bad weather occurs and this to some extent can help save lives and property. However, the determination of predictive models is a technically challenging task because rainfall is a non-linear phenomenon. In this research, a combination of a deep learning model called Convolutional Long-Short Term Memory (ConvLSTM) assisted by digital image processing is applied to real-time radar time series and satellite images. The goal is to predict the next event in a sequence of images with different timestamps i.e., 10-minutes, 30-minutes, and 60-minutes. Experimental results were evaluated with performance metrics using the Structural Similarity Index Measure (SSIM)." @default.
- W4316924467 created "2023-01-18" @default.
- W4316924467 creator A5002950551 @default.
- W4316924467 creator A5028312677 @default.
- W4316924467 creator A5064546309 @default.
- W4316924467 date "2022-10-03" @default.
- W4316924467 modified "2023-09-28" @default.
- W4316924467 title "Rainfall Nowcasting based on Satellite Images using Convolutional Long-Short Term Memory" @default.
- W4316924467 cites W2891710152 @default.
- W4316924467 cites W2893954686 @default.
- W4316924467 cites W2900707599 @default.
- W4316924467 cites W2914719353 @default.
- W4316924467 cites W2966605655 @default.
- W4316924467 cites W2977497395 @default.
- W4316924467 cites W2979728443 @default.
- W4316924467 cites W2984886732 @default.
- W4316924467 cites W3032699174 @default.
- W4316924467 cites W3049461089 @default.
- W4316924467 cites W3097194212 @default.
- W4316924467 cites W3172267363 @default.
- W4316924467 cites W3194057278 @default.
- W4316924467 doi "https://doi.org/10.1109/icset57543.2022.10010806" @default.
- W4316924467 hasPublicationYear "2022" @default.
- W4316924467 type Work @default.
- W4316924467 citedByCount "0" @default.
- W4316924467 crossrefType "proceedings-article" @default.
- W4316924467 hasAuthorship W4316924467A5002950551 @default.
- W4316924467 hasAuthorship W4316924467A5028312677 @default.
- W4316924467 hasAuthorship W4316924467A5064546309 @default.
- W4316924467 hasConcept C113954288 @default.
- W4316924467 hasConcept C119857082 @default.
- W4316924467 hasConcept C121332964 @default.
- W4316924467 hasConcept C124101348 @default.
- W4316924467 hasConcept C127413603 @default.
- W4316924467 hasConcept C146978453 @default.
- W4316924467 hasConcept C151406439 @default.
- W4316924467 hasConcept C153294291 @default.
- W4316924467 hasConcept C154945302 @default.
- W4316924467 hasConcept C162324750 @default.
- W4316924467 hasConcept C166566181 @default.
- W4316924467 hasConcept C187736073 @default.
- W4316924467 hasConcept C19269812 @default.
- W4316924467 hasConcept C205649164 @default.
- W4316924467 hasConcept C2779662365 @default.
- W4316924467 hasConcept C2780451532 @default.
- W4316924467 hasConcept C2781013037 @default.
- W4316924467 hasConcept C29825287 @default.
- W4316924467 hasConcept C41008148 @default.
- W4316924467 hasConcept C554190296 @default.
- W4316924467 hasConcept C61797465 @default.
- W4316924467 hasConcept C62520636 @default.
- W4316924467 hasConcept C76155785 @default.
- W4316924467 hasConcept C79403827 @default.
- W4316924467 hasConceptScore W4316924467C113954288 @default.
- W4316924467 hasConceptScore W4316924467C119857082 @default.
- W4316924467 hasConceptScore W4316924467C121332964 @default.
- W4316924467 hasConceptScore W4316924467C124101348 @default.
- W4316924467 hasConceptScore W4316924467C127413603 @default.
- W4316924467 hasConceptScore W4316924467C146978453 @default.
- W4316924467 hasConceptScore W4316924467C151406439 @default.
- W4316924467 hasConceptScore W4316924467C153294291 @default.
- W4316924467 hasConceptScore W4316924467C154945302 @default.
- W4316924467 hasConceptScore W4316924467C162324750 @default.
- W4316924467 hasConceptScore W4316924467C166566181 @default.
- W4316924467 hasConceptScore W4316924467C187736073 @default.
- W4316924467 hasConceptScore W4316924467C19269812 @default.
- W4316924467 hasConceptScore W4316924467C205649164 @default.
- W4316924467 hasConceptScore W4316924467C2779662365 @default.
- W4316924467 hasConceptScore W4316924467C2780451532 @default.
- W4316924467 hasConceptScore W4316924467C2781013037 @default.
- W4316924467 hasConceptScore W4316924467C29825287 @default.
- W4316924467 hasConceptScore W4316924467C41008148 @default.
- W4316924467 hasConceptScore W4316924467C554190296 @default.
- W4316924467 hasConceptScore W4316924467C61797465 @default.
- W4316924467 hasConceptScore W4316924467C62520636 @default.
- W4316924467 hasConceptScore W4316924467C76155785 @default.
- W4316924467 hasConceptScore W4316924467C79403827 @default.
- W4316924467 hasLocation W43169244671 @default.
- W4316924467 hasOpenAccess W4316924467 @default.
- W4316924467 hasPrimaryLocation W43169244671 @default.
- W4316924467 hasRelatedWork W2145515984 @default.
- W4316924467 hasRelatedWork W2164297952 @default.
- W4316924467 hasRelatedWork W2323525487 @default.
- W4316924467 hasRelatedWork W2377216019 @default.
- W4316924467 hasRelatedWork W3088903681 @default.
- W4316924467 hasRelatedWork W4205519360 @default.
- W4316924467 hasRelatedWork W4213225422 @default.
- W4316924467 hasRelatedWork W4224300826 @default.
- W4316924467 hasRelatedWork W4309045103 @default.
- W4316924467 hasRelatedWork W4321995217 @default.
- W4316924467 isParatext "false" @default.
- W4316924467 isRetracted "false" @default.
- W4316924467 workType "article" @default.