气候变化领域集成服务门户(CCPortal): Machine learning for predicting shallow groundwater levels in urban areas

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DOI	10.1016/j.jhydrol.2024.130902
	Machine learning for predicting shallow groundwater levels in urban areas
	Labianca, Ane; Koch, Julian; Jjensen, Karsten Hogh; Sonnenborg, Torben O.; Kidmose, Jacob
发表日期	2024
ISSN	0022-1694
EISSN	1879-2707
起始页码	632
卷号	632
英文摘要	In this study, the potential of machine learning (ML) for shallow groundwater level predictions in urban areas is explored. It focuses on curating a training dataset that represents the spatial variability of the water table depth, tests the effect of using different feature variables in ML modeling, and finally, compares two ML models with a physically-based (PB) urban hydrological model. To curate a consistent training dataset, a method of transferring low-frequency groundwater level measurements to a minimum water table depth (MWTD) was developed. Two ML models, one with national maps as feature variables and the other including local high-resolution urban feature variables, were trained against the same 280 groundwater level data points and applied to predict the MWTD at a 10 m spatial resolution for the city of Odense, Denmark. The ML models reached a similar fit to the observations, with an RMSE of 1.1 m and 1.3 m, respectively, and outperformed the urban PB model. In densely urbanized areas, the ML models and the PB model showed up to a 1.5 m difference in predictions of MWTD. The results suggest that ML modeling has the potential to provide spatially high-resolution predictions of the shallow groundwater table in urban areas, which represents a challenge for PB models because of their model structure and the lack of hydrological knowledge hindering meaningful parameterization schemes. Furthermore, a SHapley Additive exPlanation (SHAP) analysis of the feature variables illustrates that ML models can be utilized to explore the hydrological relations in urban domains, by analyzing the feature variables' relations.
英文关键词	Urban groundwater; Machine learning; Water table depth; CatBoost; SHAP
语种	英语
WOS研究方向	Engineering ; Geology ; Water Resources
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS记录号	WOS:001194202000001
来源期刊	JOURNAL OF HYDROLOGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/301011
作者单位	University of Copenhagen; Geological Survey Of Denmark & Greenland
推荐引用方式 GB/T 7714	Labianca, Ane,Koch, Julian,Jjensen, Karsten Hogh,et al. Machine learning for predicting shallow groundwater levels in urban areas[J],2024,632.
APA	Labianca, Ane,Koch, Julian,Jjensen, Karsten Hogh,Sonnenborg, Torben O.,&Kidmose, Jacob.(2024).Machine learning for predicting shallow groundwater levels in urban areas.JOURNAL OF HYDROLOGY,632.
MLA	Labianca, Ane,et al."Machine learning for predicting shallow groundwater levels in urban areas".JOURNAL OF HYDROLOGY 632(2024).