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Predictive pollen-based biome modeling using machine learning

This paper investigates suitability of supervised machine learning classification methods for classification of biomes using pollen datasets. We assign modern pollen samples from Africa and Arabia to five biome classes using a previously published African pollen dataset and a global ecosystem classi...

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Autores principales: Sobol, Magdalena K., Finkelstein, Sarah A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6122137/
https://www.ncbi.nlm.nih.gov/pubmed/30138366
http://dx.doi.org/10.1371/journal.pone.0202214
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author Sobol, Magdalena K.
Finkelstein, Sarah A.
author_facet Sobol, Magdalena K.
Finkelstein, Sarah A.
author_sort Sobol, Magdalena K.
collection PubMed
description This paper investigates suitability of supervised machine learning classification methods for classification of biomes using pollen datasets. We assign modern pollen samples from Africa and Arabia to five biome classes using a previously published African pollen dataset and a global ecosystem classification scheme. To test the applicability of traditional and machine-learning based classification models for the task of biome prediction from high dimensional modern pollen data, we train a total of eight classification models, including Linear Discriminant Analysis, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Classification Decision Tree, Random Forest, Neural Network, and Support Vector Machine. The ability of each model to predict biomes from pollen data is statistically tested on an independent test set. The Random Forest classifier outperforms other models in its ability correctly classify biomes given pollen data. Out of the eight models, the Random Forest classifier scores highest on all of the metrics used for model evaluations and is able to predict four out of five biome classes to high degree of accuracy, including arid, montane, tropical and subtropical closed and open systems, e.g. forests and savanna/grassland. The model has the potential for accurate reconstructions of past biomes and awaits application to fossil pollen sequences. The Random Forest model may be used to investigate vegetation changes on both long and short time scales, e.g. during glacial and interglacial cycles, or more recent and abrupt climatic anomalies like the African Humid Period. Such applications may contribute to a better understanding of past shifts in vegetation cover and ultimately provide valuable information on drivers of climate change.
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spelling pubmed-61221372018-09-18 Predictive pollen-based biome modeling using machine learning Sobol, Magdalena K. Finkelstein, Sarah A. PLoS One Research Article This paper investigates suitability of supervised machine learning classification methods for classification of biomes using pollen datasets. We assign modern pollen samples from Africa and Arabia to five biome classes using a previously published African pollen dataset and a global ecosystem classification scheme. To test the applicability of traditional and machine-learning based classification models for the task of biome prediction from high dimensional modern pollen data, we train a total of eight classification models, including Linear Discriminant Analysis, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Classification Decision Tree, Random Forest, Neural Network, and Support Vector Machine. The ability of each model to predict biomes from pollen data is statistically tested on an independent test set. The Random Forest classifier outperforms other models in its ability correctly classify biomes given pollen data. Out of the eight models, the Random Forest classifier scores highest on all of the metrics used for model evaluations and is able to predict four out of five biome classes to high degree of accuracy, including arid, montane, tropical and subtropical closed and open systems, e.g. forests and savanna/grassland. The model has the potential for accurate reconstructions of past biomes and awaits application to fossil pollen sequences. The Random Forest model may be used to investigate vegetation changes on both long and short time scales, e.g. during glacial and interglacial cycles, or more recent and abrupt climatic anomalies like the African Humid Period. Such applications may contribute to a better understanding of past shifts in vegetation cover and ultimately provide valuable information on drivers of climate change. Public Library of Science 2018-08-23 /pmc/articles/PMC6122137/ /pubmed/30138366 http://dx.doi.org/10.1371/journal.pone.0202214 Text en © 2018 Sobol, Finkelstein http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sobol, Magdalena K.
Finkelstein, Sarah A.
Predictive pollen-based biome modeling using machine learning
title Predictive pollen-based biome modeling using machine learning
title_full Predictive pollen-based biome modeling using machine learning
title_fullStr Predictive pollen-based biome modeling using machine learning
title_full_unstemmed Predictive pollen-based biome modeling using machine learning
title_short Predictive pollen-based biome modeling using machine learning
title_sort predictive pollen-based biome modeling using machine learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6122137/
https://www.ncbi.nlm.nih.gov/pubmed/30138366
http://dx.doi.org/10.1371/journal.pone.0202214
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