Assimptotic Numerical Method for Multidimensional Integrals of Forecasting of Thermokarst Lakes

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Somente assinantes

Resumo

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.

Sobre autores

Yuri Popkov

Federal Research Center “Computer Science and Control” of RAS

Autor responsável pela correspondência
Email: popkov@isa.ru

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

Rússia, Moscow

Yuri Polyschuk

Yugra Research Institute of Information Technologies

Email: yupolishchuk@gmail.com

chief research scientist, doctor of science

Rússia, Khanty-Mansyisk

Bibliografia

  1. Zuidhoff F.S., Kolstrup E. Changes in palsa distribution in relation to climate change in Laivadalen, Northern Sweden, espesially 1960-1997. Permafrost and Periglacial Processes, 2000, v.11, pp. 55-69.
  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.

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML


Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).