Validation strategies for deep learning-based groundwater level time series prediction using exogenous meteorological input features

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 achieve on completely unseen test data to provide a solid basis for model selection decisions. This paper investigates the suitability of the following performance estimation strategies (validation methods) for predicting GWL: blocked cross-validation (bl-CV), repeated out-of-sample validation (repOOS) and out-of-sample validation (OOS). The strategies are tested on an one-dimensional convolutional neural network (1D-CNN) and a long-short-term memory (LSTM) network. 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 time series 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 validation 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.