Comparing Physics‐Based, Conceptual and Machine‐Learning Models to Predict Groundwater Levels by BMA

Groundwater level observations are used as decision variables for aquifer management, often in conjunction with models to provide predictions for operational forecasting. In this study, we compare different model classes for this task: a spatially explicit 3D groundwater flow model (MODFLOW), an eig...

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Veröffentlicht in:Ground water Jg. 63; H. 4; S. 484 - 505
Hauptverfasser: Wöhling, Thomas, Delgadillo, Alvaro Oliver Crespo, Kraft, Moritz, Guthke, Anneli
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Malden, US Blackwell Publishing Ltd 01.07.2025
Ground Water Publishing Company
Schlagworte:
Aquifer management
Aquifers
Bayesian analysis
Bayesian theory
Calibration
Decision trees
Environmental Monitoring - methods
Forecasting
Groundwater
Groundwater - analysis
Groundwater flow
Groundwater levels
Learning algorithms
Machine Learning
Mathematical models
Models, Theoretical
New Zealand
Physics
Predictions
Probability theory
Research Paper
Spoil
Three dimensional flow
Water Movements
Wells
New Zealand
ISSN:0017-467X, 1745-6584, 1745-6584
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Zusammenfassung:Groundwater level observations are used as decision variables for aquifer management, often in conjunction with models to provide predictions for operational forecasting. In this study, we compare different model classes for this task: a spatially explicit 3D groundwater flow model (MODFLOW), an eigenmodel, a transfer‐function model, and three machine learning models, namely, multi‐layer perceptron models, long short‐term memory models, and random forest models. The models differ widely in their complexity, input requirements, calibration effort, and run‐times. They are tested on four groundwater level time series from the Wairau Aquifer in New Zealand to investigate the potential of the data‐driven approaches to outperform the MODFLOW model in predicting individual target wells. Further, we wish to reveal whether the MODFLOW model has advantages in predicting all four wells simultaneously because it can use the available information in a physics‐based, integrated manner, or whether structural limitations spoil this effect. Our results demonstrate that data‐driven models with low input requirements and short run‐times are competitive candidates for local groundwater level predictions even for system states that lie outside the calibration data range. There is no “single best” model that performs best in all cases, which motivates ensemble forecasting with different model classes using Bayesian model averaging. The obtained Bayesian model weights clearly favor MODFLOW when targeting all wells simultaneously, even though the competing approaches had the chance to fine‐tune for each tested well individually. This is a remarkable result that strengthens the argument for physics‐based approaches even for seemingly “simple” groundwater level prediction tasks.
Bibliographie:Article impact statement
Six model types to predict groundwater levels are compared and combined to provide guidance to modelers for their selection and use.
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Article impact statement: Six model types to predict groundwater levels are compared and combined to provide guidance to modelers for their selection and use.
ISSN:0017-467X
1745-6584
1745-6584
DOI:10.1111/gwat.13487