Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2016WR020197 |
Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification | |
Tesoriero, Anthony J.1; Gronberg, Jo Ann2; Juckem, Paul F.3; Miller, Matthew P.4; Austin, Brian P.5 | |
2017-08-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:8 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Machine learning techniques were applied to a large (n>10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide-bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000411202000051 |
WOS关键词 | WATER-QUALITY ; EASTERN WISCONSIN ; REGIONAL-SCALE ; DRINKING-WATER ; FLOW PATHS ; NITRATE REDUCTION ; CENTRAL VALLEY ; SANDY AQUIFER ; SYSTEMS ; TRENDS |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/19970 |
专题 | 资源环境科学 |
作者单位 | 1.US Geol Survey, Portland, OR 97201 USA; 2.US Geol Survey, 345 Middlefield Rd, Menlo Pk, CA 94025 USA; 3.US Geol Survey, Middleton, WI USA; 4.US Geol Survey, Salt Lake City, UT USA; 5.Wisconsin Dept Nat Resources, Madison, WI USA |
推荐引用方式 GB/T 7714 | Tesoriero, Anthony J.,Gronberg, Jo Ann,Juckem, Paul F.,et al. Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification[J]. WATER RESOURCES RESEARCH,2017,53(8). |
APA | Tesoriero, Anthony J.,Gronberg, Jo Ann,Juckem, Paul F.,Miller, Matthew P.,&Austin, Brian P..(2017).Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification.WATER RESOURCES RESEARCH,53(8). |
MLA | Tesoriero, Anthony J.,et al."Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification".WATER RESOURCES RESEARCH 53.8(2017). |
条目包含的文件 | 条目无相关文件。 |
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