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The Fourier Series Model for Predicting Sapflow Density Flux Based on TreeTalker Monitoring System

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Internet of Things, Smart Spaces, and Next Generation Networks and Systems (NEW2AN 2020, ruSMART 2020)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 12526))

Abstract

The development and application of smart technologies in various fields is increasing every year. Different monitoring systems and sensors generate a large amount of data sets which allows to solve various tasks on data prediction and classification. This paper deals with data sets generated by a new tree monitoring system which evaluates in particular the sap flow density flux describing water transport in trees. The main task consists in prediction of the values of this characteristic which reflects the tree life state based only on observable air temperature during the predictable time interval and subsequent classification of trees according to some prespecified classes. The Fourier series based model is used to fit the data sets with periodic patterns. The multivariate regression model defines the functional dependencies between sap flow density and temperature time series. The paper shows that Fourier coefficients can be successfully used as elements of the feature vectors required to solve different classification problems. Artificial multilayer neural networks are used as classifiers. The quality of the developed model for prediction and classification is verified by numerous numerical examples.

The work was supported by the Russian Science Foundation, project 19-77-30012 (recipients I. Kochetkova, A. Yarovslavtsev, R. Valentini). The publication has been prepared with the support of the “RUDN University Program 5-100” (recipients D. Efrosinin, K. Samouylov).

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Author information

Authors and Affiliations

  1. Johannes Kepler University Linz, Altenbergerstrasse 69, 4040, Linz, Austria

    Dmitry Efrosinin

  2. Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya St., Moscow, 117198, Russian Federation

    Dmitry Efrosinin, Irina Kochetkova, Alexey Yarovslavtsev, Konstantin Samouylov & Riccardo Valentini

  3. Institute of Informatics Problems, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilova St., Moscow, 119333, Russian Federation

    Irina Kochetkova & Konstantin Samouylov

  4. V.A. Trapeznikov Institute of Control Sciences of RAS, Profsoyuznaya St., 65, 117997, Moscow, Russia

    Natalia Stepanova

  5. LAMP, Russian Timiryazev State Agrarian University, 49 Timiryazevskaya st., Moscow, 127550, Russian Federation

    Alexey Yarovslavtsev

  6. Tuscia University, Via S.M. in Gradi n.4, 01100, Viterbo, Italy

    Riccardo Valentini

Authors
  1. Dmitry Efrosinin
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  2. Irina Kochetkova
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  3. Natalia Stepanova
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  4. Alexey Yarovslavtsev
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  5. Konstantin Samouylov
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  6. Riccardo Valentini
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Corresponding author

Correspondence to Irina Kochetkova .

Editor information

Editors and Affiliations

  1. Unit of Electrical Engineering, Tampere University, Tampere, Finland

    Olga Galinina

  2. Unit of Electrical Engineering, Tampere University, Tampere, Finland

    Sergey Andreev

  3. FRUCT Oy, Helsinki, Finland

    Sergey Balandin

  4. Unit of Electrical Engineering, Tampere University, Tampere, Finland

    Yevgeni Koucheryavy

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Efrosinin, D., Kochetkova, I., Stepanova, N., Yarovslavtsev, A., Samouylov, K., Valentini, R. (2020). The Fourier Series Model for Predicting Sapflow Density Flux Based on TreeTalker Monitoring System. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2020 2020. Lecture Notes in Computer Science(), vol 12526. Springer, Cham. https://doi.org/10.1007/978-3-030-65729-1_18

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  • DOI: https://doi.org/10.1007/978-3-030-65729-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65728-4

  • Online ISBN: 978-3-030-65729-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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