Deep Learning for Groundwater Prediction

This thesis explores the integration of advanced machine learning techniques, particularly deep learning, in enhancing groundwater prediction models. The primary focus is on developing new surrogate models that leverage deep neural networks for simulating groundwater flow, bridging the gap between traditional hydrological methods and contemporary data science approaches. The research journey begins with the application of synthetic data and computer vision techniques and progressively advances towards handling sparse data and real-world scenarios. The thesis comprises four key papers, each contributing to the development of machine learning models for groundwater prediction. These models include convolutional encoder-decoder networks (Attention U-Net and U-Net integrated with Vision Transformer) for accurate steady-state response prediction, the DeepONet framework for generalized groundwater flow modeling under data-sparse scenarios, and finally spatial-temporal graph neural networks for long-term forecasting of groundwater levels. The research demonstrates the ability of these models to handle complex hydrological systems, predict accurately under varying conditions, and efficiently process both high-dimensional inputs and sparse data. Overall, this thesis contributes to the field of hydrology by establishing advanced machine learning models as viable alternatives for predictive groundwater level modeling, particularly noted for their accuracy, computational efficiency, and adaptability to diverse scenarios. The findings pave the way for future research, focusing on applying these models to larger and more complex datasets for practical use in groundwater management and decision-making

Developing a cost-effective emulator for groundwater flow modeling using deep neural operators

Current groundwater models face a significant challenge in their implementation due to heavy computational burdens. To overcome this, our work proposes a cost-effective emulator that efficiently and accurately forecasts the impact of abstraction in an aquifer. Our approach uses a deep neural operator (DeepONet) to learn operators that map between infinite-dimensional function spaces via deep neural networks. The goal is to infer the distribution of hydraulic head in a confined aquifer in the presence of a pumping well. We successfully tested the DeepONet on four problems, including two forward problems, an inverse analysis, and a nonlinear system. Additionally, we propose a novel extension of the DeepONet-based architecture to generate accurate predictions for varied hydraulic conductivity fields and pumping well locations that are unseen during training. Our emulator's predictions match the target data with excellent performance, demonstrating that the proposed model can act as an efficient and fast tool to support a range of tasks that require repetitive forward numerical simulations or inverse simulations of groundwater flow problems. Overall, our work provides a promising avenue for developing cost-effective and accurate groundwater models

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

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

Attention U-Net as a surrogate model for groundwater prediction

Numerical simulations of groundwater flow are used to analyze and predict the response of an aquifer system to its change in state by approximating the solution of the fundamental groundwater physical equations. The most used and classical methodologies, such as Finite Difference (FD) and Finite Element (FE) Methods, use iterative solvers which are associated with high computational cost. This study proposes a physics-based convolutional encoder-decoder neural network as a surrogate model to quickly calculate the response of the groundwater system. Holding strong promise in cross-domain mappings, encoder-decoder networks are applicable for learning complex input-output mappings of physical systems. This manuscript presents an Attention U-Net model that attempts to capture the fundamental input-output relations of the groundwater system and generates solutions of hydraulic head in the whole domain given a set of physical parameters and boundary conditions. The model accurately predicts the steady state response of a highly heterogeneous groundwater system given the locations and piezometric head of up to 3 wells as input. The network learns to pay attention only in the relevant parts of the domain and the generated hydraulic head field corresponds to the target samples in great detail. Even relative to coarse finite difference approximations the proposed model is shown to be significantly faster than a comparative state-of-the-art numerical solver, thus providing a base for further development of the presented networks as surrogate models for groundwater prediction

Investigation of the influence of animal burrowing on the failure of the levee of San Matteo along the Secchia river

Animal burrowing can greatly influence the water pressures in a flood protection embankment and thereby be a cause of breaching of flood defences. However, little guidance and literature is available on this subject. This paper investigates the contribution of badgers, porcupines and foxes to the failure of the levee of San Matteo (Modena, Italy) on 19th January 2014. The proposed method evaluates their influence on the water pressures in the embankment during rainfall and a high water tide. The influence of the burrowing is assessed through a transient FEM flow analysis. Starting from the documented entrances situated in the vadose zone, different scenarios for the internal distribution of tunnels and chambers are proposed. The most likely representative network for the loss of stability of the dike is assessed

Study of the effect of burrows of European Badgers (Meles meles) on the initiation of breaching in dikes

Levees offer an ideal environment for the diffusion of European Badgers (Meles Meles), whose burrowing activity can be a main cause of breaching in dikes. This paper evaluates the contribution of burrows dug by badgers to the initiation of a dike failure. For example, a breach in a dike near Modena (Italy), where animal burrowing activity was active, took place on the 19th January 2014. While the distribution of the underground system inside the dike is usually unknown, the burrow entrances are easily identified along the slopes. Different scenarios of burrow entrances located along the outer and inner slope are considered and their contribution to sliding of the inner slope, micro-instability and internal erosion is investigated. The most dangerous positions of entrances for the stability of the dike are assessed and presented

Developing a cost-effective emulator for groundwater flow modeling using deep neural operators

Current groundwater models face significant challenges in their implementation due to heavy computational burdens. To overcome this, our work proposes a cost-effective emulator that efficiently and accurately forecasts the impact of abstraction in an aquifer. Our approach uses a deep neural operator (DeepONet) framework to learn operators that map between infinite-dimensional function spaces via deep neural networks. The goal is to infer the distribution of hydraulic heads in a confined aquifer in the presence of a pumping well. We successfully tested the DeepONet framework on multiple problems, including forward time-dependent problems, an inverse analysis, and a nonlinear system. Additionally, we propose a novel extension of the DeepONet-based architecture to generate accurate predictions for varied hydraulic conductivity fields and pumping well locations that are unseen during training. Our emulator’s predictions match the target data with excellent performance, demonstrating that the proposed model can act as an efficient and fast tool to support a range of tasks that require repetitive forward numerical simulations or inverse simulations of groundwater flow problems. Overall, our work provides a promising avenue for developing cost-effective and accurate groundwater models