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Dr Charlie Kirkwood

Publications

_{Copyright Notice: Any articles made available for download are for personal use only. Any other use requires prior permission of the author and the copyright holder.}

| 2023 | 2022 | 2021 | 2020 | 2018 | 2016 |

2023

• Kirkwood C. (2023) Methods in machine learning for probabilistic modelling of environment, with applications in meteorology and geology.

2022

• Kirkwood C, Economou T, Odbert H, Pugeault N. (2022) A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data. [PDF]
• Kirkwood C, Economou T, Pugeault N, Odbert H. (2022) Bayesian Deep Learning for Spatial Interpolation in the Presence of Auxiliary Information, Mathematical Geosciences, volume 54, no. 3, pages 507-531, DOI:10.1007/s11004-021-09988-0. [PDF]

2021

• Kirkwood C, Economou T, Odbert H, Pugeault N. (2021) Bayesian deep learning for large scale environmental data modelling. [PDF]
• Haupt SE, Chapman W, Adams SV, Kirkwood C, Hosking JS, Robinson NH, Lerch S, Subramanian AC. (2021) Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, volume 379, no. 2194, pages 20200091-20200091, DOI:10.1098/rsta.2020.0091. [PDF]
• Kirkwood C, Economou T, Odbert H, Pugeault N. (2021) A framework for probabilistic weather forecast post-processing across models and lead times using machine learning, Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, volume 379, no. 2194, DOI:10.1098/rsta.2020.0099.

2020

• Kirkwood C, Economou T, Pugeault N. (2020) Bayesian deep learning for mapping via auxiliary information: a new era for geostatistics?. [PDF]
• Kirkwood C. (2020) Deep covariate-learning: optimising information extraction from terrain texture for geostatistical modelling applications. [PDF]

2018

• Ferreira A, Daraktchieva Z, Beamish D, Kirkwood C, Lister TR, Cave M, Wragg J, Lee K. (2018) Indoor radon measurements in south west England explained by topsoil and stream sediment geochemistry, airborne gamma-ray spectroscopy and geology, JOURNAL OF ENVIRONMENTAL RADIOACTIVITY, volume 181, pages 152-171, DOI:10.1016/j.jenvrad.2016.05.007. [PDF]

2016

• Kirkwood C, Everett P, Ferreira A, Lister B. (2016) Stream sediment geochemistry as a tool for enhancing geological understanding: An overview of new data from south west England, JOURNAL OF GEOCHEMICAL EXPLORATION, volume 163, pages 28-40, DOI:10.1016/j.gexplo.2016.01.010. [PDF]
• Kirkwood C, Cave M, Beamish D, Grebby S, Ferreira A. (2016) A machine learning approach to geochemical mapping, JOURNAL OF GEOCHEMICAL EXPLORATION, volume 167, pages 49-61, DOI:10.1016/j.gexplo.2016.05.003. [PDF]

Showing 11 publications from Symplectic.