MIREA – Russian technological university (applied mathematics, professor)
Russian Federation
graduate student
Russian Federation
UDC 005
UDC 502/504
An algorithm for studying the avalanche hazard of a territory based on a nearly periodic analysis of data proposed, which results in intervals of uniform function behavior. These intervals divided by uniformly spaced boundaries that form a rectangular grid in the space of linearized data. An algorithm for studying the avalanche hazard of a mountainous territory proposed, based on a nearly periodic analysis of linearized data obtained during the polygonal transformation of the original satellite image of a mountain range. Based on the results of applying the algorithm, a composition of uniform longitudinal and transverse intervals of uniform behavior of linearized data is determined, forming a system of critical spatial barriers of the original image that determine the degree of avalanche hazard of nearby territories.
avalanche, danger, near period, satellite image
1. Snow avalanche in the Indian Himalayas: Hazard zonation and climate change trends in Kullu region of Himachal Pradesh, India / J.K. Bansal [et al.] // Physics and Chemistry of the Earth, Parts A/B/C. 2025. Vol. 138. P. 103882. DOI:https://doi.org/10.1016/j.pce.2025.103882
2. Review of spatial variability of snowpack properties and its importance for avalanche formation / J. Schweizer [et al.] // Cold Regions Science and Technology. 2008. Vol. 51. Iss. 2–3. P. 253–272. DOI: 1016/j.coldregions.2007.04.009
3. Lavinnaya aktivnost' v Rossii v usloviyah izmenyayushchegosya klimata / A.S. Turchaninova [i dr.] // Vestnik Rossijskogo fonda fundamental'nyh issledovanij. 2022. № 3–4 (115–116). S. 122–131.
4. Ricinskij reliktovyj nacional'nyj park. Federaciya al'pinizma i skalolazaniya. Sneg i laviny v gorah. Prognoz i bezopasnost'. Gudauta: RRNP, 2024. 36 s.
5. Barbolini M., Keylock C. A new method for avalanche hazard mapping using a combination of statistical and deterministic models // Natural Hazards and Earth System Sciences. 2002. Vol. 2. P. 239–245. DOI:https://doi.org/10.5194/nhess-2-239-2002
6. Singh A., Ganju A. A supplement to nearest-neighbour method for avalanche forecasting // Cold Regions Science and Technology. 2004. Vol. 39. Iss. 2–3. P. 105–113. DOI:https://doi.org/10.1016/j.coldregions.2004.03.005
7. Snow avalanche hazard prediction using machine learning methods / B. Choubin [et al.] // Journal of Hydrology. 2019. Vol. 577. P. 123929. DOI:https://doi.org/10.1016/j.jhydrol.2019.123929
8. Chen M., Mao S., Liu Yu. Big data: survey // Mobile Netw Appl. 2014. Vol. 19 (2). P. 171–209.
9. Jaseena K.U., David J.M. Issues, challenges, and solutions: big data mining // Comput Sci Inf Technol (CS & IT). 2014. Vol. 4. P. 131–40.
10. Big data analytics: a survey / C.W. Tsai [et al.] // J Big Data. 2015. Vol. 2 (1). P. 21.
11. Programma poligonal'nogo razbieniya izobrazheniy s ob'ektami nelineynoy struktury: svidetel'stvo o gosudarstvennoy registracii programmy dlya EVM № 2024688034 / Kuznecova K.A., Kalach A.V., Paramonov A.A., Smolenceva T.E., Kryneckiy B.A; zareg. 05.11.2024; opubl. 25.11.2024.
12. Programma poligonal'nogo razbieniya izobrazhenij s ob"ektami nelinejnoj struktury: svidetel'stvo o gosudarstvennoj registracii programmy dlya EVM № 2024688034 / Kuznecova K.A., Kalach A.V., Paramonov A.A., Smolenceva T.E., Kryneckij B.A; zareg. 05.11.2024; opubl. 25.11.2024.




