FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4292651749> ?p ?o ?g. }

• W4292651749 endingPage "5" @default.
• W4292651749 startingPage "1" @default.
• W4292651749 abstract "Near-real time estimation of precipitation from geostationary satellites plays a vital role in natural disaster mitigation due to timely monitoring, high spatial-temporal resolution and large coverage, yet this research remains a large challenge. In this research, a novel Deep Learning-based algorithm entitled Precipitation Estimation using a Multi-Scale network (DLPE-MS) is proposed to estimate precipitation during summer over eastern Continental United States (CONUS) of America. When inputting bispectral satellite information (10.3 &#x03BC;m and 6.2 &#x03BC;m), this algorithm provides near-real time rainfall rates at hourly and 0.04&#x00B0;&#x00D7;0.04&#x00B0; resolution. In order to emphasize the information of a precipitation region at different scales using satellites&#x2019; data, we design a multi-scale framework based on convolutional neural networks (CNNs). In addition, a loss function named Balanced Weights Mean Square Error (BWMSE) is proposed to settle the problem of underestimation caused by a shortage in heavy rainy samples. Compared with the Mean Square Error (MSE), the BWMSE has more balance parameters for different objects when training, which is able to mitigate the deviation between the prediction and ground truth in tailed categories (heavy rainy). Results show that this algorithm achieves the highest Probability of Detection (POD) and Correlation Coefficient (CC) with the value of 95.5% and 0.5. The statistical results of the precipitation cases also show that the DLPE-MS can significantly improve the estimated values in tailed categories than other products and methods. After testing, this algorithm is able to estimate the precipitation for the study area within 0.19 seconds." @default.
• W4292651749 created "2022-08-22" @default.
• W4292651749 creator A5006971815 @default.
• W4292651749 creator A5033737940 @default.
• W4292651749 creator A5073323121 @default.
• W4292651749 creator A5076808723 @default.
• W4292651749 creator A5077905824 @default.
• W4292651749 creator A5078166358 @default.
• W4292651749 date "2022-01-01" @default.
• W4292651749 modified "2023-09-24" @default.
• W4292651749 title "Improvement of a Near-Real-Time Precipitation Estimation Algorithm Using Deep Learning" @default.
• W4292651749 cites W1522265489 @default.
• W4292651749 cites W1901129140 @default.
• W4292651749 cites W1995622703 @default.
• W4292651749 cites W2022548072 @default.
• W4292651749 cites W2034563918 @default.
• W4292651749 cites W2055308682 @default.
• W4292651749 cites W2059116274 @default.
• W4292651749 cites W2094653192 @default.
• W4292651749 cites W2101394945 @default.
• W4292651749 cites W2119803205 @default.
• W4292651749 cites W2138644576 @default.
• W4292651749 cites W2139347645 @default.
• W4292651749 cites W2204227166 @default.
• W4292651749 cites W2328573691 @default.
• W4292651749 cites W2559001317 @default.
• W4292651749 cites W2560268323 @default.
• W4292651749 cites W2590366910 @default.
• W4292651749 cites W2614464134 @default.
• W4292651749 cites W2784731895 @default.
• W4292651749 cites W2900303043 @default.
• W4292651749 cites W2919115771 @default.
• W4292651749 cites W2974510043 @default.
• W4292651749 cites W2977666392 @default.
• W4292651749 cites W3023506241 @default.
• W4292651749 cites W3082059999 @default.
• W4292651749 cites W3096052669 @default.
• W4292651749 cites W3210169729 @default.
• W4292651749 cites W65738273 @default.
• W4292651749 doi "https://doi.org/10.1109/lgrs.2022.3200756" @default.
• W4292651749 hasPublicationYear "2022" @default.
• W4292651749 type Work @default.
• W4292651749 citedByCount "2" @default.
• W4292651749 countsByYear W42926517492022 @default.
• W4292651749 countsByYear W42926517492023 @default.
• W4292651749 crossrefType "journal-article" @default.
• W4292651749 hasAuthorship W4292651749A5006971815 @default.
• W4292651749 hasAuthorship W4292651749A5033737940 @default.
• W4292651749 hasAuthorship W4292651749A5073323121 @default.
• W4292651749 hasAuthorship W4292651749A5076808723 @default.
• W4292651749 hasAuthorship W4292651749A5077905824 @default.
• W4292651749 hasAuthorship W4292651749A5078166358 @default.
• W4292651749 hasConcept C104110773 @default.
• W4292651749 hasConcept C105795698 @default.
• W4292651749 hasConcept C107054158 @default.
• W4292651749 hasConcept C11413529 @default.
• W4292651749 hasConcept C127313418 @default.
• W4292651749 hasConcept C127413603 @default.
• W4292651749 hasConcept C139945424 @default.
• W4292651749 hasConcept C146849305 @default.
• W4292651749 hasConcept C146978453 @default.
• W4292651749 hasConcept C153294291 @default.
• W4292651749 hasConcept C154945302 @default.
• W4292651749 hasConcept C16405173 @default.
• W4292651749 hasConcept C19269812 @default.
• W4292651749 hasConcept C197046000 @default.
• W4292651749 hasConcept C205649164 @default.
• W4292651749 hasConcept C33923547 @default.
• W4292651749 hasConcept C41008148 @default.
• W4292651749 hasConcept C62649853 @default.
• W4292651749 hasConcept C81363708 @default.
• W4292651749 hasConceptScore W4292651749C104110773 @default.
• W4292651749 hasConceptScore W4292651749C105795698 @default.
• W4292651749 hasConceptScore W4292651749C107054158 @default.
• W4292651749 hasConceptScore W4292651749C11413529 @default.
• W4292651749 hasConceptScore W4292651749C127313418 @default.
• W4292651749 hasConceptScore W4292651749C127413603 @default.
• W4292651749 hasConceptScore W4292651749C139945424 @default.
• W4292651749 hasConceptScore W4292651749C146849305 @default.
• W4292651749 hasConceptScore W4292651749C146978453 @default.
• W4292651749 hasConceptScore W4292651749C153294291 @default.
• W4292651749 hasConceptScore W4292651749C154945302 @default.
• W4292651749 hasConceptScore W4292651749C16405173 @default.
• W4292651749 hasConceptScore W4292651749C19269812 @default.
• W4292651749 hasConceptScore W4292651749C197046000 @default.
• W4292651749 hasConceptScore W4292651749C205649164 @default.
• W4292651749 hasConceptScore W4292651749C33923547 @default.
• W4292651749 hasConceptScore W4292651749C41008148 @default.
• W4292651749 hasConceptScore W4292651749C62649853 @default.
• W4292651749 hasConceptScore W4292651749C81363708 @default.
• W4292651749 hasFunder F4320321001 @default.
• W4292651749 hasFunder F4320335777 @default.
• W4292651749 hasLocation W42926517491 @default.
• W4292651749 hasOpenAccess W4292651749 @default.
• W4292651749 hasPrimaryLocation W42926517491 @default.
• W4292651749 hasRelatedWork W2020667207 @default.
• W4292651749 hasRelatedWork W2024862785 @default.
• W4292651749 hasRelatedWork W2109237945 @default.

• next