{"title":"使用机器学习方法预测降水中稳定同位素的空间分布:随机森林变异的比较评估","authors":"D. Erdélyi, Z. Kern, T. Nyitrai, I. Hatvani","doi":"10.1007/s13137-023-00224-x","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":44484,"journal":{"name":"GEM-International Journal on Geomathematics","volume":" ","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Predicting the spatial distribution of stable isotopes in precipitation using a machine learning approach: a comparative assessment of random forest variants\",\"authors\":\"D. Erdélyi, Z. Kern, T. Nyitrai, I. Hatvani\",\"doi\":\"10.1007/s13137-023-00224-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":44484,\"journal\":{\"name\":\"GEM-International Journal on Geomathematics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2023-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"GEM-International Journal on Geomathematics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s13137-023-00224-x\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"GEM-International Journal on Geomathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13137-023-00224-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Predicting the spatial distribution of stable isotopes in precipitation using a machine learning approach: a comparative assessment of random forest variants