99 citations · 223 across the 14 of their papers we have counts for
15 papers
Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data
Abigail Langbridge, Fearghal O'Donncha, James T Rayfield +1
Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based framework for anomaly detectio…
Machine Learning in Fish Farming
Fearghal O'Donncha, Nikos Papandroulakis, Jennie Korus +10
This chapter explores how machine learning (ML) is transforming aquaculture, with a particular focus on enhancing decision-making processes and improving operational efficiency. Th…
AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance
Dhaval Patel, Shuxin Lin, James Rayfield +7
AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows, such as condition monitoring and maintenance scheduling, to minimize system downtime. W…
Towards Automated Solution Recipe Generation for Industrial Asset Management with LLM
Nianjun Zhou, Dhaval Patel, Shuxin Lin +1
This study introduces a novel approach to Industrial Asset Management (IAM) by incorporating Conditional-Based Management (CBM) principles with the latest advancements in Large Lan…
Causal Temporal Graph Convolutional Neural Networks (CTGCN)
Abigail Langbridge, Fearghal O'Donncha, Amadou Ba +3
Many large-scale applications can be elegantly represented using graph structures. Their scalability, however, is often limited by the domain knowledge required to apply them. To a…
A SWAT-based Reinforcement Learning Framework for Crop Management
Malvern Madondo, Muneeza Azmat, Kelsey Dipietro +5
Crop management involves a series of critical, interdependent decisions or actions in a complex and highly uncertain environment, which exhibit distinct spatial and temporal variat…
Attention-based Domain Adaptation Forecasting of Streamflow in Data-Sparse Regions
Roland Oruche, Fearghal O'Donncha
Streamflow forecasts are critical to guide water resource management, mitigate drought and flood effects, and develop climate-smart infrastructure and governance. Many global regio…
Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management
Muneeza Azmat, Malvern Madondo, Kelsey Dipietro +5
Climate change, population growth, and water scarcity present unprecedented challenges for agriculture. This project aims to forecast soil moisture using domain knowledge and machi…
Transfer learning to improve streamflow forecasts in data sparse regions
Roland Oruche, Lisa Egede, Tracy Baker +1
Effective water resource management requires information on water availability, both in terms of quality and quantity, spatially and temporally. In this paper, we study the methodo…
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales
Yihao Hu, Fearghal O'Donncha, Paulito Palmes +3
This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series forecasting applied to environmental datasets. The framework was evaluated across multiple s…
Statistical and machine learning ensemble modelling to forecast sea surface temperature
Stefan Wolff, Fearghal O'Donncha, Bei Chen
In situ and remotely sensed observations have potential to facilitate data-driven predictive models for oceanography. A suite of machine learning models, including regression, deci…
Ensemble model aggregation using a computationally lightweight machine-learning model to forecast ocean waves
Fearghal O'Donncha, Yushan Zhang, Bei Chen +1
This study investigated an approach to improve the accuracy of computationally lightweight surrogate models by updating forecasts based on historical accuracy relative to sparse ob…
Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting
Philipp Haehnel, Jakub Marecek, Julien Monteil +1
Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs). Although the use of deep-learning techniques has been proposed, act…
Localised sequential state estimation for advection dominated flows with non-Gaussian uncertainty description
Emanuele Ragnoli, Mykhaylo Zayats, Fearghal O'Donncha +1
This paper presents a new iterative state estimation algorithm for advection dominated flows with non-Gaussian uncertainty description of -type: uncertain initial conditi…
A Machine Learning Framework to Forecast Wave Conditions
Scott C. James, Yushan Zhang, Fearghal O'Donncha
A~machine learning framework is developed to estimate ocean-wave conditions. By supervised training of machine learning models on many thousands of iterations of a physics-based wa…