Yang Li, Siyuan Huo, Bin Ma, Bingbing Pei, Qiankun Tan, Qing Guo, Deng Wang, Longbiao Yu
{"title":"基于深度学习和时空聚类的降水δ18O分布预测新方法","authors":"Yang Li, Siyuan Huo, Bin Ma, Bingbing Pei, Qiankun Tan, Qing Guo, Deng Wang, Longbiao Yu","doi":"10.1080/02626667.2024.2375403","DOIUrl":null,"url":null,"abstract":"Predicting precipitation δ18O accurately is crucial for understanding water cycles, paleoclimates, and hydrological applications. Yet, forecasting its spatio-temporal distribution remains challengi...","PeriodicalId":13036,"journal":{"name":"Hydrological Sciences Journal","volume":"41 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A new method for predicting precipitation δ18O distribution based on deep learning and spatio-temporal clustering\",\"authors\":\"Yang Li, Siyuan Huo, Bin Ma, Bingbing Pei, Qiankun Tan, Qing Guo, Deng Wang, Longbiao Yu\",\"doi\":\"10.1080/02626667.2024.2375403\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predicting precipitation δ18O accurately is crucial for understanding water cycles, paleoclimates, and hydrological applications. Yet, forecasting its spatio-temporal distribution remains challengi...\",\"PeriodicalId\":13036,\"journal\":{\"name\":\"Hydrological Sciences Journal\",\"volume\":\"41 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-02\",\"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/10.1080/02626667.2024.2375403\",\"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/10.1080/02626667.2024.2375403","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new method for predicting precipitation δ18O distribution based on deep learning and spatio-temporal clustering
Predicting precipitation δ18O accurately is crucial for understanding water cycles, paleoclimates, and hydrological applications. Yet, forecasting its spatio-temporal distribution remains challengi...
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
