Articles | Volume 20, issue 10
https://doi.org/10.5194/cp-20-2373-2024
https://doi.org/10.5194/cp-20-2373-2024
Research article
 | 
24 Oct 2024
Research article |  | 24 Oct 2024

Can machine-learning algorithms improve upon classical palaeoenvironmental reconstruction models?

Peng Sun, Philip B. Holden, and H. John B. Birks

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Cited articles

Allott, T. E. H., Harriman, R., and Battarbee, R. W.: Reversibility of lake acidification at the Round Loch of Glenhead, Galloway, Scotland, Environ. Pollut., 77, 219–225, https://doi.org/10.1016/0269-7491(92)90080-T, 1992. 
Battarbee, R. W., Monteith, D. T., Juggins, S., Evans, C. D., Jenkins, A., and Simpson, G. L.: Reconstructing pre-acidification pH for an acidified Scottish loch: A comparison of palaeolimnological and modelling approaches, Environ. Pollut., 137, 135–149, https://doi.org/10.1016/j.envpol.2004.12.021, 2005. 
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Birks, H. J. B.: Numerical tools in palaeolimnology – Progress, potentialities, and problems, J. Paleolimnol., 20, 307–332, https://doi.org/10.1023/A:1008038808690, 1998. 
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Short summary
We develop the Multi Ensemble Machine Learning Model (MEMLM) for reconstructing palaeoenvironments from microfossil assemblages. The machine-learning approaches, which include random tree and natural language processing techniques, substantially outperform classical approaches under cross-validation, but they can fail when applied to reconstruct past environments. Statistical significance testing is found sufficient to identify these unreliable reconstructions.