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Modeling a Potential Plant Habitat by Ensemble Machine Learning

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High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production (HPCST 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1733))

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Abstract

The article is devoted to modeling the potential distribution habitat of Pulsatilla turczaninovii Kull. et Serg. (Turchaninova prostrate). Modeling of ecological niches of plants is the process of building models using modern computer algorithms and bioclimatic data to predict the habitat distribution of plant species. The result of modeling is a model that can be used to map the area where species grow or live, predict the habitat, or analyze the impact of the environment on species. Building effective models for predicting plant ecological niches requires data on both the presence and absence of species in an area. Species absence points (or background points) are not recorded in databases but can be generated using different approaches. This article describes the implementation of three approaches to selecting pseudo-absence points in a given area: 1) randomly selecting from all points in a given area, excluding existing points of presence; 2) randomly selecting any point located at least one degree of latitude or longitude from any point of presence; 3) random selection of points from all points outside the suitable area estimated based on bioclimatic variables. The article presents the result of modeling the potential distribution range of the species Pulsatilla turczaninovii Kull. et Serg. using the random forest algorithm, the most popular method of constructing ensembles of decision trees. The software implementation of the model is carried out in the high-level programming language Python.

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Acknowledgments

This work was supported in the framework of the “Priority-2030” Program by Altai State University.

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Authors and Affiliations

  1. South-Siberian Botanical Garden, Altai State University, Lenin Avenue 61, 656049, Barnaul, Russia

    Alexei V. Vaganov & Vladimir F. Zaikov

  2. Altai State University, Lenin Avenue 61, 656049, Barnaul, Russia

    Olga S. Krotova, Andrey I. Musokhranov, Zakhar V. Pokalyakin & Lyubov A. Khvorova

Authors
  1. Alexei V. Vaganov
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  2. Vladimir F. Zaikov
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  3. Olga S. Krotova
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  4. Andrey I. Musokhranov
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  5. Zakhar V. Pokalyakin
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  6. Lyubov A. Khvorova
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Corresponding author

Correspondence to Lyubov A. Khvorova .

Editor information

Editors and Affiliations

  1. Altai State University, Barnaul, Russia

    Vladimir Jordan

  2. MIREA - Russian Technological University, Moscow, Russia

    Ilya Tarasov

  3. Novosibirsk State Technical University, Novosibirsk, Russia

    Ella Shurina

  4. Lomonosov Moscow State University, Moscow, Russia

    Nikolay Filimonov

  5. Tomsk State University of Control Systems and Radio-Electronics, Tomsk, Russia

    Vladimir Faerman

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Vaganov, A.V., Zaikov, V.F., Krotova, O.S., Musokhranov, A.I., Pokalyakin, Z.V., Khvorova, L.A. (2023). Modeling a Potential Plant Habitat by Ensemble Machine Learning. In: Jordan, V., Tarasov, I., Shurina, E., Filimonov, N., Faerman, V. (eds) High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production. HPCST 2022. Communications in Computer and Information Science, vol 1733. Springer, Cham. https://doi.org/10.1007/978-3-031-23744-7_16

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  • DOI: https://doi.org/10.1007/978-3-031-23744-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23743-0

  • Online ISBN: 978-3-031-23744-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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