气候变化领域集成服务门户(CCPortal): Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning

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DOI	10.1088/1748-9326/ab7d5c
	Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning
	Knoll L.; Breuer L.; Bach M.
发表日期	2020
ISSN	17489318
卷号	15 期号:6
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 × 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 R2 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. © 2020 The Author(s). Published by IOP Publishing Ltd.
英文关键词	groundwater quality; large-scale; nitrate pollution; random forest; redox conditions; uncertainty
语种	英语
scopus关键词	Decision trees; Environmental regulations; Groundwater; Machine learning; Nitrates; Random forests; Water conservation; Water quality; Water resources; European Water Framework Directive; Groundwater monitoring networks; Machine learning techniques; Nitrate concentration; Predictive performance; Protection of water resources; Spatial environmental informations; Uncertainty assessment; Water pollution; concentration (composition); groundwater; machine learning; nitrate; redox conditions; water pollution; Germany
来源期刊	Environmental Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/154006
作者单位	Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Giessen, Germany
推荐引用方式 GB/T 7714	Knoll L.,Breuer L.,Bach M.. Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning[J],2020,15(6).
APA	Knoll L.,Breuer L.,&Bach M..(2020).Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning.Environmental Research Letters,15(6).
MLA	Knoll L.,et al."Nation-wide estimation of groundwater redox conditions and nitrate concentrations through machine learning".Environmental Research Letters 15.6(2020).