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DOI	10.5194/hess-23-4603-2019
	Modelling of the shallow water table at high spatial resolution using random forests
	Koch J.; Berger H.; Henriksen H.J.; Sonnenborg T.O.
发表日期	2019
ISSN	1027-5606
起始页码	4603
结束页码	4619
卷号	23 期号:11
英文摘要	Machine learning provides great potential for modelling hydrological variables at a spatial resolution beyond the capabilities of physically based modelling. This study features an application of random forests (RF) to model the depth to the shallow water table, for a wintertime minimum event, at a 50 m resolution over a 15 000 km2 domain in Denmark. In Denmark, the shallow groundwater poses severe risks with respect to groundwater-induced flood events, affecting both urban and agricultural areas. The risk is especially critical in wintertime, when the shallow groundwater is close to terrain. In order to advance modelling capabilities of the shallow groundwater system and to provide estimates at the scales required for decision-making, this study introduces a simple method to unify RF and physically based modelling. Results from the national water resources model in Denmark (DK-model) at a 500 m resolution are employed as covariates in the RF model. Thus, RF ensures physical consistency at a coarse scale and fully exhausts high-resolution information from readily available environmental variables. The vertical distance to the nearest water body was rated as the most important covariate in the trained RF model followed by the DK-model. The evaluation test of the trained RF model was very satisfying with a mean absolute error of 76 cm and a coefficient of determination of 0.56. The resulting map underlines the severity of groundwater flooding risk in Denmark, as the average depth to the shallow groundwater is 1.9 m and approximately 29 % of the area is characterized as having a depth of less than 1 m during a typical wintertime minimum event. This study brings forward a novel method for assessing the spatial patterns of covariate importance of the RF predictions that contributes to an increased interpretability of the RF model. Quantifying the uncertainty of RF models is still rare for hydrological applications. Two approaches, namely random forests regression kriging (RFRK) and quantile regression forests (QRF), were tested to estimate uncertainties related to the predicted groundwater levels. © 2019 Author(s).
语种	英语
scopus关键词	Decision making; Decision trees; Floods; Image resolution; Uncertainty analysis; Water resources; Environmental variables; Groundwater-induced floods; High spatial resolution; Hydrological variables; Modelling capabilities; Physically-based Modelling; Shallow water tables; Water resources modeling; Groundwater; algorithm; decision making; flooding; groundwater; hydrological modeling; kriging; machine learning; shallow water; spatial resolution; water resource; water table; Denmark
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159570
作者单位	Koch, J., Department of Hydrology Geological Survey of Denmark and Greenland (GEUS), Copenhagen, 1350, Denmark; Berger, H., COWI A/S, Lyngby, 2800, Denmark; Henriksen, H.J., Department of Hydrology Geological Survey of Denmark and Greenland (GEUS), Copenhagen, 1350, Denmark; Sonnenborg, T.O., Department of Hydrology Geological Survey of Denmark and Greenland (GEUS), Copenhagen, 1350, Denmark
推荐引用方式 GB/T 7714	Koch J.,Berger H.,Henriksen H.J.,et al. Modelling of the shallow water table at high spatial resolution using random forests[J],2019,23(11).
APA	Koch J.,Berger H.,Henriksen H.J.,&Sonnenborg T.O..(2019).Modelling of the shallow water table at high spatial resolution using random forests.Hydrology and Earth System Sciences,23(11).
MLA	Koch J.,et al."Modelling of the shallow water table at high spatial resolution using random forests".Hydrology and Earth System Sciences 23.11(2019).