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

ABSTRACT: 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,

SUBMITTER: Wohling T 

PROVIDER: S-EPMC12272009 | biostudies-literature | 2025 Jul-Aug

REPOSITORIES: biostudies-literature