NDLI: A comparison between diatom-based pH inference models using Artificial Neural Networks (ANN), Weighted Averaging (WA) and Weighted Averaging Partial Least Squares (WA-PLS) regressions

Content Provider	Springer Nature Link
Author	Racca, Julien M.J. Philibert, Aline Racca, Robert Prairie, Yves T.
Copyright Year	2001
Abstract	We explored the possibility of using artificial neural networks (ANN) to develop quantitative inference models in paleolimnology. ANNs are dynamic computer systems able to learn the relations between input and output data. We developed ANN models to infer pH from fossil diatom assemblages using a calibration data set of 76 lakes in Quebec. We evaluated the predictive power of these models in comparison with the two most commonly methods used in paleolimnology: Weighted Averaging (WA) and Weighted Averaging Partial Least Squares (WA-PLS). Results show that the relationship between species assemblages and environmental variables of interest can be modelled by a 3-layer back-propagation network, with apparent R$^{2}$ and RMSE of 0.9 and 0.24 pH units, respectively. Leave-one-out cross-validation was used to access the reliabilities of the WA, WA-PLS and ANN models. Validation results show that the ANN model (R$^{2}$ $_{jackknife}$ = 0.63, RMSE$_{jackknife}$ = 0.45, mean bias = 0.14, maximum bias = 1.13) gives a better predictive power than the WA model (R$^{2}$ $_{jackknife}$ = 0.56, RMSE$_{jackknife}$ = 0.5, mean bias = −0.09, maximum bias = −1.07) or WA-PLS model (R$^{2}$ $_{jackknife}$ = 0.58, RMSE$_{jackknife}$ = 0.48, mean bias = −0.15, maximum bias = −1.08). We also evaluated whether the removal of certain taxa according to their tolerance changed the performance of the models. Overall, we found that the removal of taxa with high tolerances for pH improved the predictive power of WA-PLS models whereas the removal of low tolerance taxa lowered its performance. However, ANN models were generally much less affected by the removal of taxa of either low or high pH tolerance. Moreover, the best model was obtained by averaging the predictions of WA-PLS and ANN models. This implies that the two modelling approaches capture and extract complementary information from diatom assemblages. We suggest that future modelling efforts might achieve better results using analogous multi-model strategies.
Starting Page	411
Ending Page	422
Page Count	12
File Format	PDF
ISSN	09212728
Journal	Journal of Paleolimnology
Volume Number	26
Issue Number	4
e-ISSN	15730417
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2001-01-01
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Sedimentology Physical Geography Hydrobiology Climate Change
Content Type	Text
Resource Type	Article
Subject	Earth-Surface Processes Aquatic Science

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