Preprints
https://doi.org/10.5194/egusphere-2025-3539
https://doi.org/10.5194/egusphere-2025-3539
08 Sep 2025
 | 08 Sep 2025
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Validation Strategies for Deep Learning-Based Groundwater Level Time Series Prediction Using Exogenous Meteorological Input Features

Fabienne Doll, Tanja Liesch, Maria Wetzel, Stefan Kunz, and Stefan Broda

Abstract. Due to the growing reliance on machine learning (ML) approaches for predicting groundwater levels (GWL), it is important to examine the methods used for performance estimation. A suitable performance estimation method provides the most accurate estimate of the accuracy the model would archive on completely unseen test data to provide a solid basis for model selection decisions. This paper investigates the suitability of different performance evaluation strategies, namely blocked cross-validation (bl-CV), repeated out-of-sample validation (repOOS), and out-of-sample validation (OOS), for evaluating one-dimensional convolutional neural network (1D-CNN) models for predicting groundwater level (GWL) using exogenous meteorological input data. Unlike previous comparative studies, which mainly focused on autoregressive models, this work uses a non-autoregressive approach based on exogenous meteorological input features without incorporating past groundwater levels for groundwater level prediction. A dataset of 100 GWL time series was used to evaluate the performance of the different validation methods. The study concludes that bl-CV provides the most representative performance estimates of actual model performance compared to the other two performance evaluation methods examined. The most commonly used OOS validation yielded the most uncertain performance estimate in this study. The results underscore the importance of carefully selecting a performance estimation strategy to ensure that model comparisons and adjustments are made on a reliable basis.

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Fabienne Doll, Tanja Liesch, Maria Wetzel, Stefan Kunz, and Stefan Broda

Status: open (until 03 Nov 2025)

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Fabienne Doll, Tanja Liesch, Maria Wetzel, Stefan Kunz, and Stefan Broda
Fabienne Doll, Tanja Liesch, Maria Wetzel, Stefan Kunz, and Stefan Broda
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Short summary
With the growing use of machine learning for groundwater level (GWL) prediction, proper performance estimation is crucial. This study compares three validation strategies—blocked cross-validation (bl-CV), repeated out-of-sample (repOOS), and out-of-sample (OOS)—for 1D-CNN models using meteorological inputs. Results show that bl-CV offers the most reliable performance estimates, while OOS is the most uncertain, highlighting the need for careful method selection.
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