Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1088/1748-9326/ab7d5c |
Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning | |
Knoll, Lukas; Breuer, Lutz; Bach, Martin | |
2020-06-01 | |
发表期刊 | ENVIRONMENTAL RESEARCH LETTERS |
ISSN | 1748-9326 |
出版年 | 2020 |
卷号 | 15期号:6 |
文章类型 | Article |
语种 | 英语 |
国家 | Germany |
英文摘要 | The protection of water resources and development of mitigation strategies require large-scale information on water pollution such as nitrate. Machine learning techniques like random forest (RF) have proven their worth for estimating groundwater quality based on spatial environmental predictors. We investigate the potential of RF and quantile random forest (QRF) to estimate redox conditions and nitrate concentration in groundwater (1 km x 1 km resolution) using the European Water Framework Directive groundwater monitoring network as well as spatial environmental information available throughout Germany. The RF model for nitrate achieves a good predictive performance with an R-2 of 0.52. Dominant predictors are the redox conditions in the groundwater body, hydrogeological units and the percentage of arable land. An uncertainty assessment using QRF shows rather large uncertainties with a mean prediction interval (MPI) of 53.0 mg l(-1). This study represents the first nation-wide data-driven assessment of the spatial distribution of groundwater nitrate concentrations for Germany. |
英文关键词 | groundwater quality nitrate pollution redox conditions random forest uncertainty large-scale |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000536880700001 |
WOS关键词 | CENTRAL VALLEY ; QUANTILE REGRESSION ; FEATURE-SELECTION ; RANDOM FOREST ; DENITRIFICATION ; UNCERTAINTY ; PREDICTION ; FRAMEWORK ; POLLUTION ; AQUIFER |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
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/279337 |
专题 | 气候变化 |
作者单位 | Justus Liebig Univ Giessen, Res Ctr BioSyst Land Use & Nutr iFZ, Inst Landscape Ecol & Resources Management ILR, Giessen, Germany |
推荐引用方式 GB/T 7714 | Knoll, Lukas,Breuer, Lutz,Bach, Martin. Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning[J]. ENVIRONMENTAL RESEARCH LETTERS,2020,15(6). |
APA | Knoll, Lukas,Breuer, Lutz,&Bach, Martin.(2020).Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning.ENVIRONMENTAL RESEARCH LETTERS,15(6). |
MLA | Knoll, Lukas,et al."Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning".ENVIRONMENTAL RESEARCH LETTERS 15.6(2020). |
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