Cesar Ambrogi Ferreira do Lago, Jose Artur Teixeira Brasil, Marcus Nóbrega Gomes Junior, Eduardo Mario Mendiondo, Marcio H. Giacomoni
{"title":"利用深度学习提高大型粗模型的冲积洪水测绘分辨率","authors":"Cesar Ambrogi Ferreira do Lago, Jose Artur Teixeira Brasil, Marcus Nóbrega Gomes Junior, Eduardo Mario Mendiondo, Marcio H. Giacomoni","doi":"10.1080/02626667.2024.2329268","DOIUrl":null,"url":null,"abstract":"Deep Learning (DL) models are a promising complement to hydrodynamic models. However, the application of DL for detailed predictions on large domains has not yet been tested. We aim to narrow addre...","PeriodicalId":13036,"journal":{"name":"Hydrological Sciences Journal","volume":"20 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving pluvial flood mapping resolution of large coarse models with deep learning\",\"authors\":\"Cesar Ambrogi Ferreira do Lago, Jose Artur Teixeira Brasil, Marcus Nóbrega Gomes Junior, Eduardo Mario Mendiondo, Marcio H. Giacomoni\",\"doi\":\"10.1080/02626667.2024.2329268\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep Learning (DL) models are a promising complement to hydrodynamic models. However, the application of DL for detailed predictions on large domains has not yet been tested. We aim to narrow addre...\",\"PeriodicalId\":13036,\"journal\":{\"name\":\"Hydrological Sciences Journal\",\"volume\":\"20 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Hydrological Sciences Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/02626667.2024.2329268\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hydrological Sciences Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/02626667.2024.2329268","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving pluvial flood mapping resolution of large coarse models with deep learning
Deep Learning (DL) models are a promising complement to hydrodynamic models. However, the application of DL for detailed predictions on large domains has not yet been tested. We aim to narrow addre...
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
