Training deep learning models with a multi-station approach and static aquifer attributes for groundwater level simulation: what is the best way to leverage regionalised information?
Sivarama Krishna Reddy Chidepudi, Nicolas Masséi, Abderrahim Jardani, Bastien Dieppois, Abel Henriot, Matthieu Fournier
Abstract
Abstract. In this study, we use deep learning models with advanced variants of recurrent neural networks, specifically long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM), to simulate large-scale groundwater level (GWL) fluctuations in northern France. We develop multi-station collective training for GWL simulations, using dynamic variables (i.e. climatic) and static basin characteristics. This large-scale approach can incorporate dynamic and static features to cover more reservoir heterogeneities in the study area. Further, we investigated the performance of relevant feature extraction techniques such as clustering and wavelet transform decomposition to simplify network learning using regionalised information. Several modelling performance tests were conducted. Models specifically trained on different types of GWL, clustered based on the spectral properties, performed significantly better than models trained on the whole dataset. Clustering-based modelling reduces complexity in the training data and targets relevant information more efficiently. Applying multi-station models without prior clustering can lead the models to preferentially learn the dominant behaviour, ignoring unique local variations. In this respect, wavelet pre-processing was found to partially compensate for clustering, bringing out common temporal and spectral characteristics shared by all available GWL time series even when these characteristics are “hidden” (e.g. if their amplitude is too small). When employed along with prior clustering, using wavelet decomposition as a pre-processing technique significantly improves model performances, particularly for GWLs dominated by low-frequency interannual to decadal variations. This study advances our understanding of GWL simulation using deep learning, highlighting the importance of different model training approaches, the potential of wavelet pre-processing, and the value of incorporating static attributes.