99 citations · 223 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…