Book of Abstracts

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Multilevel Delayed Acceptance MCMC with Applications to Hydrogeological Inverse Problems

Quantifying the uncertainty of model predictions is a critical task for engineering decision support systems. This is a particularly challenging effort in the context of statistical inverse problems, where the model parameters are unknown or poorly constrained, and where the data is often scarce. Many such problems emerge in the fields of hydrology and hydro--environmental engineering in general, and in hydrogeology in particular. While methods for rigorously quantifying the uncertainty of such problems exist, they are often prohibitively computationally expensive, particularly when the forward model is high--dimensional and expensive to evaluate. In this thesis, I present a Metropolis--Hastings algorithm, namely the Multilevel Delayed Acceptance (MLDA) algorithm, which exploits a hierarchy of forward models of increasing computational cost to significantly reduce the total cost of quantifying the uncertainty of high--dimensional, expensive forward models. The algorithm is shown to be in detailed balance with the posterior distribution of parameters, and the computational gains of the algorithm is demonstrated on multiple examples. Additionally, I present an approach for exploiting a deep neural network as an ultra--fast model approximation in an MLDA model hierarchy. This method is demonstrated in the context of both 2D and 3D groundwater flow modelling. Finally, I present a novel approach to adaptive optimal design of groundwater surveying, in which MLDA is employed to construct the posterior Monte Carlo estimates. This method utilises the posterior uncertainty of the primary problem in conjunction with the expected solution to an adjoint problem to sequentially determine the optimal location of the next datapoint.Engineering and Physical Sciences Research Council (EPSRC)Alan Turing InstituteEngineering and Physical Sciences Research Council (EPSRC

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

State Of the Art Report in the fields of numerical analysis and scientific computing. Final version as of 16/02/2020 deliverable D4.1 of the HORIZON 2020 project EURAD.: European Joint Programme on Radioactive Waste Management

Document information Project Acronym EURAD Project Title European Joint Programme on Radioactive Waste Management Project Type European Joint Programme (EJP) EC grant agreement No. 847593 Project starting / end date 1 st June 2019-30 May 2024 Work Package No. 4 Work Package Title Development and Improvement Of NUmerical methods and Tools for modelling coupled processes Work Package Acronym DONUT Deliverable No. 4.

Development of Real-Time Surface Water Abstraction Management Tools

Efficient use of available water resources to meet demand, whilst maintaining the quality of the aquatic environment has become increasingly important. Water quality challenges associated with diffuse agricultural pollutions have also become widely recognized problems globally. This thesis presents the development of new approaches to improve surface water abstraction management with a view to mitigate the challenges associated with increasing pressures on availability of water resources for public water supply and diffuse agricultural pollution. The first part of the thesis presents the development of a real-time surface water abstraction management scheme that integrates a conceptual rainfall-runoff model, a Bayesian inference based uncertainty analysis tool and a water resources management model that incorporates various operating rules to represent real-world operational constraints. The developed approach enables efficient utilization of available water resources and thus provides improved capability to deal with emerging issues of increasing demand, climate adaptation planning and associated policy reforms. The second part of the thesis describes the development of a new travel time based physically distributed metaldehyde prediction model, which enables water infrastructure operators to consider informed surface water abstraction decisions. Metaldehyde is a soluble synthetic aldehyde pesticide used globally in agriculture and has caused recent concerns due to high observed levels in surface waters utilized for potable water supply. The model provides new approach to represent spatially and temporally disaggregated runoff generation, routing and build-up/wash-off processes using a grid based structure in a GIS environment. Furthermore, a state-of-the-art Monte Carlo based spatial uncertainty analysis tool is employed to assess uncertainties in the metaldehyde prediction model. The structure of the metaldehyde model combined with the availability of high spatiotemporal resolution data has enabled the application of spatial uncertainty analysis of the catchment scale metaldehyde model, which is currently lacking in water quality modelling studies

Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences

Numerical Techniques for Optimization Problems with PDE Constraints

The development, analysis and implementation of efficient and robust numerical techniques for optimization problems associated with partial differential equations (PDEs) is of utmost importance for the optimal control of processes and the optimal design of structures and systems in modern technology. The successful realization of such techniques invokes a wide variety of challenging mathematical tasks and thus requires the application of adequate methodologies from various mathematical disciplines. During recent years, significant progress has been made in PDE constrained optimization both concerning optimization in function space according to the paradigm ’Optimize first, then discretize’ and with regard to the fast and reliable solution of the large-scale problems that typically arise from discretizations of the optimality conditions. The contributions at this Oberwolfach workshop impressively reflected the progress made in the field. In particular, new insights have been gained in the analysis of optimal control problems for PDEs that have led to vastly improved numerical solution methods. Likewise, breakthroughs have been made in the optimal design of structures and systems, for instance, by the socalled ’all-at-once’ approach featuring simultaneous optimization and solution of the underlying PDEs. Finally, new methodologies have been developed for the design of innovative materials and the identification of parameters in multi-scale physical and physiological processes

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