Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling

Maria Luisa Taccari, Oded Ovadia, He Wang, Adar Kahana, Xiaohui Chen, Peter K. Jimack

Abstract: This paper presents a comprehensive comparison of various machine learning models, namely U-Net, U-Net integrated with Vision Transformers (ViT), and Fourier Neural Operator (FNO), for time-dependent forward modelling in groundwater systems. Through testing on synthetic datasets, it is demonstrated that U-Net and U-Net + ViT models outperform FNO in accuracy and efficiency, especially in sparse data scenarios. These findings underscore the potential of U-Net-based models for groundwater modelling in real-world applications where data scarcity is prevalent.