| A machine learning framework to forecast wave conditions SC James, Y Zhang, F O'Donncha Coastal Engineering 137, 1-10, 2018 | 378 | 2018 |
| Statistical and machine learning ensemble modelling to forecast sea surface temperature S Wolff, F O'Donncha, B Chen Journal of Marine Systems 208 (103347), 2020 | 90 | 2020 |
| Precision aquaculture F O'donncha, J Grant IEEE Internet of Things Magazine 2 (4), 26-30, 2019 | 86 | 2019 |
| Characterizing observed circulation patterns within a bay using HF radar and numerical model simulations F O’Donncha, M Hartnett, S Nash, L Ren, E Ragnoli Journal of Marine Systems 142, 96-110, 2015 | 74 | 2015 |
| Physical and numerical investigation of the hydrodynamic implications of aquaculture farms F O’Donncha, M Hartnett, S Nash Aquacultural engineering 52, 14-26, 2013 | 70 | 2013 |
| Using Deep Learning to Extend the Range of Air-Pollution Monitoring and Forecasting P Haehnel, J Marecek, J Monteil, F O'Donncha Journal of Computational Physics 408 (1), 109278, 2020 | 59 | 2020 |
| An integrated framework that combines machine learning and numerical models to improve wave-condition forecasts F O’Donncha, Y Zhang, B Chen, SC James Journal of Marine Systems 186, 29-36, 2018 | 59 | 2018 |
| Ensemble model aggregation using a computationally lightweight machine-learning model to forecast ocean waves F O’Donncha, Y Zhang, B Chen, SC James Journal of Marine Systems 199 (1), 103206, 2019 | 46 | 2019 |
| A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales F O'Donncha, Y Hu, P Palmes, M Burke, R Filgueira, J Grant Ecological Informatics 69, 101687, 2022 | 38* | 2022 |
| Data driven insight into fish behaviour and their use for precision aquaculture F O'Donncha, CL Stockwell, SR Planellas, G Micallef, P Palmes, C Webb, ... Frontiers in Animal Science 2, 695054, 2021 | 36 | 2021 |
| Modelling study of the effects of suspended aquaculture installations on tidal stream generation in Cobscook Bay F O'Donncha, SC James, E Ragnoli Renewable Energy 102, 65-76, 2017 | 30 | 2017 |
| Parallelisation study of a three-dimensional environmental flow model F O'Donncha, E Ragnoli, F Suits Computers & Geosciences 64, 96-103, 2014 | 29 | 2014 |
| Parameterizing suspended canopy effects in a three-dimensional hydrodynamic model F O'Donncha, M Hartnett, DR Plew Journal of Hydraulic Research 53 (6), 714-727, 2015 | 23 | 2015 |
| Designing environmentally efficient aquafeeds through the use of multicriteria decision support tools R Cooney, AHL Wan, F O'Donncha, E Clifford Current Opinion in Environmental Science & Health 23, 100276, 2021 | 15 | 2021 |
| An optimal interpolation scheme for assimilation of HF radar current data into a numerical ocean model E Ragnoli, F O'Donncha, S Zhuk, F Suits, M Hartnett Oceans, 2012, 1-5, 2012 | 13 | 2012 |
| Transfer learning to improve streamflow forecasts in data sparse regions R Oruche, L Egede, T Baker, F O'Donncha arXiv preprint arXiv:2112.03088, 2021 | 11 | 2021 |
| Drag coefficient parameter estimation for aquaculture systems SC James, F O’Donncha Environmental Fluid Mechanics 19 (1), 989–1003, 2019 | 11 | 2019 |
| On the efficiency of executing hydro-environmental models on cloud F O’Donncha, E Ragnoli, S Venugopal, SC James, K Katrinis Procedia Engineering 154, 199-206, 2016 | 11 | 2016 |
| Dynamic adaption of vessel trajectory using machine learning models F O'donncha, E Ragnoli, C Sutherland US Patent 11,119,250, 2021 | 10 | 2021 |
| Surrogate modeling and risk-based analysis for solute transport simulations E Arandia, F O’Donncha, S McKenna, S Tirupathi, E Ragnoli Stochastic Environmental Research and Risk Assessment 33 (11), 1907-1921, 2019 | 9 | 2019 |
Emanuele RagnoliIBM Research ScientistBestätigte E-Mail-Adresse bei ie.ibm.com