Machine learning for predicting shallow groundwater levels in urban areas

Ane LaBianca, Julian Koch, Karsten Høgh Jensen, Torben O. Sonnenborg, Jacob Kidmose

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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.

Original languageEnglish
Article number130902
Number of pages13
JournalJournal of Hydrology
Volume632
DOIs
Publication statusPublished - Mar 2024

Keywords

  • CatBoost
  • Machine learning
  • SHAP
  • Urban groundwater
  • Water table depth

Programme Area

  • Programme Area 2: Water Resources

Fingerprint

Dive into the research topics of 'Machine learning for predicting shallow groundwater levels in urban areas'. Together they form a unique fingerprint.

Cite this