{"title":"How widespread is fluoride contamination of Ghana’s groundwater?","authors":"Dahyann Araya, J. Podgorski, M. Berg","doi":"10.53014/ogjs9699","DOIUrl":null,"url":null,"abstract":"Spatial modelling using machine-learning methods to predict fluoride occurrence in groundwater has vast potential in data-scarce contexts such as Ghana. Hazard risk maps can help inform health policy targeted towards communities at high risk.","PeriodicalId":393895,"journal":{"name":"Water Science Policy","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Science Policy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.53014/ogjs9699","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Spatial modelling using machine-learning methods to predict fluoride occurrence in groundwater has vast potential in data-scarce contexts such as Ghana. Hazard risk maps can help inform health policy targeted towards communities at high risk.