Tayeb Boulmaiz, Radia Hafsi, Mawloud Guermoui, Hamouda Boutaghane, Habib Abida, Mohamed Saber, Sameh A. Kantoush, Khaled Ferkous, Yves Tramblay
{"title":"对卫星降雨产品进行预处理,利用机器学习改进水文模拟","authors":"Tayeb Boulmaiz, Radia Hafsi, Mawloud Guermoui, Hamouda Boutaghane, Habib Abida, Mohamed Saber, Sameh A. Kantoush, Khaled Ferkous, Yves Tramblay","doi":"10.1080/02626667.2024.2378108","DOIUrl":null,"url":null,"abstract":"A new preprocessing methodology for gridded satellite precipitation products (SPP) is developed to improve the performance of Machine Learning (ML) algorithms using this type of input data for runo...","PeriodicalId":13036,"journal":{"name":"Hydrological Sciences Journal","volume":"31 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Preprocessing satellite rainfall products improves hydrological simulations with machine learning\",\"authors\":\"Tayeb Boulmaiz, Radia Hafsi, Mawloud Guermoui, Hamouda Boutaghane, Habib Abida, Mohamed Saber, Sameh A. Kantoush, Khaled Ferkous, Yves Tramblay\",\"doi\":\"10.1080/02626667.2024.2378108\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new preprocessing methodology for gridded satellite precipitation products (SPP) is developed to improve the performance of Machine Learning (ML) algorithms using this type of input data for runo...\",\"PeriodicalId\":13036,\"journal\":{\"name\":\"Hydrological Sciences Journal\",\"volume\":\"31 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-09\",\"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/10.1080/02626667.2024.2378108\",\"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/10.1080/02626667.2024.2378108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Preprocessing satellite rainfall products improves hydrological simulations with machine learning
A new preprocessing methodology for gridded satellite precipitation products (SPP) is developed to improve the performance of Machine Learning (ML) algorithms using this type of input data for runo...