Assimptotic Numerical Method for Multidimensional Integrals of Forecasting of Thermokarst Lakes

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Abstract

We develop an analytical method for the approximate calculation of multidimensional integrals, focused on solving balance equations in Randomized Machine Learning procedures. The latter are used to forecast the evolution of thermokarst lakes’ area. The method is based on the series expansion of an analytical function - the exponential - and the transformation of multidimensional integrals into the product of simple one-dimensional integrals on interval sets.

About the authors

Yuri S. Popkov

Federal Research Center “Computer Science and Control” of RAS

Author for correspondence.
Email: popkov@isa.ru

chief research scientist, academician of Russian Academy of Sciences, doctor of science, professor

Russian Federation, Moscow

Yuri M. Polyschuk

Yugra Research Institute of Information Technologies

Email: yupolishchuk@gmail.com

chief research scientist, doctor of science

Russian Federation, Khanty-Mansyisk

References

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  2. Kirpotin S., Polishchuk Y., Bruksina N. Abrupt changes of thermokarst lakes in Western Siberia: impacts of climatic warming on permafrost melting. International Journal of Environmental Studies. 2009, v. 66, No.4, pp.423-431.
  3. Karlson J.M., Lyon S.W., Destouni G. Temporal behavior of lake size-distribution in a thawing permafrost landscape in Northwestern Siberia. Remote sensing, 2014, No. 6, pp. 621-636.
  4. Bryksina N.A., Polishchuk Yu.M. Analysis of changes in the number of thermokarst lakes in permafrost of Western Siberia on the basis of satellite images. Cryosphere of Earth, 2015, v. 19, No.2, pp. 114-120.
  5. Popkov Y.S., Popkov A.Y., Dubnov Y.A. Entropy randomization in Machine Learning. CRC Press, 2023
  6. Dubnov Y.A., Popkov A.Y., Polishcuk V.Y., Sokol E.S., Melnikov A.V., Y.M.Polishcuk Y.M., Popkov Y.S. Randomized Mashine Learning Algoritms to Forecast the Evolution of Thermokarst Lakes Area in Permafrost Zones. Automation and Remote Control, 2023, v.84, No.1, p. 56-70.
  7. Darkhovsky B.S., Popkov A.Y., Popkov Y.S. Method of Batch Monte Carlo Iterations for Solving of Global Optimization Problems // Informacionnie Technologii i Vichislitelnie Sistemy, 2014, No.3. pp. 39-52.

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