Russian Federation
Moscow, Moscow, Russian Federation
Russian Federation
Russian Federation
Russian Federation
Russian Federation
Russian Federation
UDK 55 Геология. Геологические и геофизические науки
UDK 550.34 Сейсмология
UDK 550.383 Главное магнитное поле Земли
GRNTI 37.01 Общие вопросы геофизики
GRNTI 37.15 Геомагнетизм и высокие слои атмосферы
GRNTI 37.25 Океанология
GRNTI 37.31 Физика Земли
GRNTI 38.01 Общие вопросы геологии
GRNTI 36.00 ГЕОДЕЗИЯ. КАРТОГРАФИЯ
GRNTI 37.00 ГЕОФИЗИКА
GRNTI 38.00 ГЕОЛОГИЯ
GRNTI 39.00 ГЕОГРАФИЯ
GRNTI 52.00 ГОРНОЕ ДЕЛО
OKSO 05.00.00 Науки о Земле
BBK 26 Науки о Земле
TBK 63 Науки о Земле. Экология
BISAC SCI SCIENCE
The work is devoted to the problem of forecasting the evolution of the area of thermokarst lakes in the Arctic permafrost zone using the analysis of test areas from several geographical regions. The approach proposed in the work is based on the Randomized Machine Learning method for constructing mathematical models of lake area from climate indicators, learning the model on real data and further forecasting. The results of modeling the dynamics of lake areas using linear static and dynamic models are presented and compared. It is shown that proposed dynamic model can significantly reduce the average modeling error
thermokarst lakes, information entropy, randomized machine learning, static and dynamic models, missing data, randomized forecasting, climate change
1. Dubnov Yu. A. Entropiynoe ocenivanie v zadachah klassifikacii // Avtomatika i telemehanika. — 2019. — № 3. — S. 138—151. — DOI:https://doi.org/10.1134/S0005231019030097.
2. Dubnov Yu. A. i Bulychev A. V. Bayesovskaya identifikaciya smeshannyh gaussovskih modeley // Informacionnye tehnologii i vychislitel'nye sistemy. — 2017. — № 1. — S. 101—114.
3. Dubnov Yu. A., Polischuk V. Yu., Popkov A. Yu. i dr. Entropiyno-randomizirovannoe prognozirovanie evolyucii ploschadi termokarstovyh ozer // Chelyabinskiy fiziko-matematicheskiy zhurnal. — 2021a. — T. 6, № 3. — S. 384—396. — DOI:https://doi.org/10.47475/2500-0101-2021-16312.
4. Dubnov Yu. A., Polischuk V. Yu., Popkov Yu. S. i dr. Metod entropiyno-randomizirovannogo vosstanovleniya propu- schennyh dannyh // Avtomatika i telemehanika. — 2021b. — S. 140—160. — DOI:https://doi.org/10.31857/S0005231021040061.
5. Dubnov Yu. A., Popkov A. Yu., Polischuk V. Yu. i dr. Algoritmy randomizirovannogo mashinnogo obucheniya dlya prognozirovaniya evolyucii ploschadi termokarstovyh ozer v zonah vechnoy merzloty // Avtomatika i telemehanika. — 2023. — № 1. — DOI:https://doi.org/10.31857/S0005231023010051.
6. Mironov M. S., Shornikova A. V., Sidorina I. E. i dr. Izuchenie emissii metana v termokarstovyh ozerah poluostrova Yamal s pomosch'yu metodov distancionnogo zondirovaniya Zemli i nazemnyh issledovaniy // Materialy 20-y Mezhdunarodnoy konferencii «Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa». — Moskva : Institut kosmicheskih issledovaniy RAN, 2022. — DOI:https://doi.org/10.21046/20DZZconf-2022a.
7. Polischuk V. Yu., Muratov I. N., Kupriyanov M. A. i dr. Modelirovanie poley termokarstovyh ozer v zone vechnoy merzloty na osnove geoimitacionnogo podhoda i sputnikovyh snimkov // Matematicheskie zametki SVFU. — 2020. — T. 27, № 1. — S. 101—114. — DOI:https://doi.org/10.25587/SVFU.2020.75.78.007.
8. Polischuk V. Yu., Muratov I. N. i Polischuk Yu. M. Problemy modelirovaniya prostranstvennoy struktury poley termokarstovyh ozer v zone vechnoy merzloty na osnove sputnikovyh snimkov // Vestnik Yugorskogo gosudarstvennogo universiteta. — 2018. — T. 3, № 50. — S. 88—100. — DOI:https://doi.org/10.17816/byusu2018088-100.
9. Popkov A. Yu. Randomizirovannoe mashinnoe obuchenie nelineynyh modeley s primeneniem k prognozirovaniyu razvitiya epidemicheskogo processa // Avtomatika i telemehanika. — 2021. — № 6. — S. 149—168. — DOI:https://doi.org/10.31857/S0005231021060064.
10. Popkov Yu. S. Randomizaciya i entropiya v obrabotke dannyh, dinamicheskih sistemah, mashinnom obuchenii. — Moskva : LENAND, 2023.
11. Popkov Yu. S. i Dubnov Yu. A. Entropiyno-robastnoe randomizirovannoe prognozirovanie pri malyh ob'emah retrospektivnyh dannyh // Avtomatika i telemehanika. — 2016. — № 5. — S. 109—127.
12. Popkov Yu. S., Dubnov Yu. A. i Popkov A. Yu. Prognozirovanie razvitiya epidemii COVID-19 v stranah Evropeyskogo soyuza s ispol'zovaniem entropiyno-randomizirovannogo podhoda // Informatika i avtomatizaciya. — 2021. — T. 20, № 5. — S. 1010—1033. — DOI:https://doi.org/10.15622/20.5.1.
13. Popkov Yu. S., Popkov A. Yu. i Dubnov Yu. A. Randomizirovannoe mashinnoe obuchenie pri ogranichennyh naborah dannyh: ot empiricheskoy veroyatnosti k entropiynoy randomizacii. — Moskva : LENAND, 2019.
14. Popkov Yu. S., Popkov A. Yu. i Dubnov Yu. A. Elementy randomizirovannogo prognozirovaniya i ego primenenie dlya predskazaniya sutochnoy elektricheskoy nagruzki energeticheskoy sistemy // Avtomatika i telemehanika. — 2020. — № 7. — S. 148—172. — DOI:https://doi.org/10.1134/S0005231019070107.
15. Stepanenko V. M., Machul'skaya E. E., Glagolev M. V. i dr. Modelirovanie emissii metana iz ozer zony vechnoy merzloty // Izvestiya RAN. Fizika atmosfery i okeana. — 2011. — T. 47, № 2. — S. 275—288.
16. Feller V. Vvedenie v teoriyu veroyatnostey i ee prilozheniya. — Moskva : Mir, 1967.
17. Fel'dman G. M. Termokarst i vechnaya merzlota. — Novosibirsk : Nauka, 1984.
18. Golan A., Judge G. G. and Miller D. Maximum entropy econometrics. Robust estimation with limited data. — New York : Wiley, 1996. — 307 p.
19. Jaynes E. T. Information theory and statistical mechanics // Physical review. — 1957. — Vol. 106, no. 4. — P. 620–630.
20. Kapur J. N. Maximum-entropy models in science and engineering. — John Wiley & Sons, 1989.
21. Karlsson J., Lyon S. and Destouni G. Temporal Behavior of Lake Size-Distribution in a Thawing Permafrost Landscape in Northwestern Siberia // Remote Sensing. — 2014. — Vol. 6, no. 1. — P. 621–636. — DOI:https://doi.org/10.3390/rs6010621.
22. Kirpotin S. N., Polishchuk Y. and Bryksina N. Abrupt changes of thermokarst lakes in Western Siberia: impacts of climatic warming on permafrost melting // International Journal of Environmental Studies. — 2009. — Vol. 66,no. 4. — P. 423–431. — DOI:https://doi.org/10.1080/00207230902758287.
23. Kullback S. and Leibler R. A. On information and sufciency // The annals of mathematical statistics. — 1951. — Vol. 22, no. 1. — P. 79–86.
24. Levine R. D. and Tribus M. Maximum entropy formalism // Maximum Entropy Formalism Conference. — Massachusetts Institute of Technology, 1978.
25. Neumann J. von. Various Techniques Used in Connection With Random Digits // Journal of Research of the National Bureau of Standards, Appl. Math. Series. — 1951. — Vol. 12. — P. 36–38.
26. Nocedal J. and Wright S. Numerical optimization. — Springer Science & Business Media, 2006.
27. Popkov Yu. S., Dubnov Yu. and Popkov A. New Method of Randomized Forecasting Using Entropy-Robust Estimation: Application to the World Population Prediction // Mathematics. — 2016. — Vol. 4, no. 1. — P. 16. — DOI:https://doi.org/10.3390/math4010016.
28. Popkov Yu. S., Popkov A. Yu., Dubnov Yu. A., et al. Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models // Mathematics. — 2020. — Vol. 8, no. 7. — P. 1119. — DOI:https://doi.org/10.3390/math8071119.
29. Popkov Yu. S., Popkov A. Yu., Dubnov Yu. A., et al. Entropy Randomization in Machine Learning. — Chapman, Hall/CRC, 2022. — DOI:https://doi.org/10.1201/9781003306566.
30. Sudakov I. and Vakulenko S. A. A mathematical model for a positive permafrost carbon-climate feedback // IMA Journal of Applied Mathematics. — 2014. — Vol. 80, no. 3. — P. 811–824. — DOI:https://doi.org/10.1093/imamat/hxu010.
31. Verpoorter Ch., Kutser T., Seekell D. A., et al. A global inventory of lakes based on high-resolution satellite imagery // Geophysical Research Letters. — 2014. — Vol. 41, no. 18. — P. 6396–6402. — DOI:https://doi.org/10.1002/2014GL060641.
32. Walter K. M., Smith L. C. and Stuart Chapin F. Methane bubbling from northern lakes: present and future contributions to the global methane budget // Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. — 2007. — Vol. 365, no. 1856. — P. 1657–1676. — DOI:https://doi.org/10.1098/rsta.2007.2036.
33. Zabelina S. A., Shirokova L. S., Klimov S. I., et al. Carbon emission from thermokarst lakes in NE European tundra // Limnology and Oceanography. — 2020. — Vol. 66, S1. — DOI:https://doi.org/10.1002/lno.11560.
