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Ushbu maqolada Amudaryo daryosining suv sarfi maksimal qiymatlarini ehtimollik usullariga asoslanib tahlil qilish masalasi ko‘rib chiqilgan. Ekstremal qiymatlarning qaytish davri bilan bog‘liqligi Gumbel taqsimoti asosida aniqlangan. Mazkur yondashuv toshqin xavfini baholash hamda gidrotexnik inshootlarni loyihalash jarayonida muhim ahamiyat kasb etadi.

  • Web Address
  • DOI10.70769/2181-2845.IJT.18.1.2025.31
  • Date of creation in the UzSCI system 30-05-2025
  • Read count 27
  • Date of publication 30-05-2025
  • Main LanguageO'zbek
  • Pages93-98
Ўзбек

Ushbu maqolada Amudaryo daryosining suv sarfi maksimal qiymatlarini ehtimollik usullariga asoslanib tahlil qilish masalasi ko‘rib chiqilgan. Ekstremal qiymatlarning qaytish davri bilan bog‘liqligi Gumbel taqsimoti asosida aniqlangan. Mazkur yondashuv toshqin xavfini baholash hamda gidrotexnik inshootlarni loyihalash jarayonida muhim ahamiyat kasb etadi.

Ўзбек

В данной статье рассматривается задача анализа максимальных значений расхода воды реки Амударья с использованием методов теории вероятностей. Связь экстремальных значений со временем возврата определена на основе распределения Гумбеля. Этот подход имеет важное значение при оценке риска наводнений и в процессе проектирования гидротехнических сооружений.

Ўзбек

This article addresses the issue of analyzing the maximum water flow values of the Amu Darya River using probability methods. The relationship between extreme values and their return periods is determined based on the Gumbel distribution. This approach is crucial for flood risk assessment and the design process of hydraulic structures.

№ Author name position Name of organisation
1 Egamov M. . Dotsent Qarshi davlat texnika universiteti
№ Name of reference
1 Bezak, N., Brilly, M., & Šraj, M. (2014). Comparison between the Gumbel, Pearson Type III and Generalized Extreme Value distributions in flood frequency analysis. Water Resources Management, 28(7), 2057–2074. https://doi.org/10.1007/s11269-014-0601-5
2 Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer Series in Statistics. London: Springer.
3 Gumbel, E. J
4 (1958). Statistics of Extremes. Columbia University Press.
5 Krysanova, V., Hattermann, F. F., Huang, S., Vetter, T., & Bronstert, A. (2015). Modelling climate and land-use change impacts with SWIM: Lessons learnt and challenges ahead. Hydrological Sciences Journal, 60(7–8), 1440–1454. https://doi.org/10.1080/02626667.2014.925560
6 Katz, R. W., Parlange, M. B., & Naveau, P. (2002). Statistics of extremes in hydrology. Advances in Water Resources, 25(8–12), 1287–1304. https://doi.org/10.1016/S0309-1708(02)00056-8
7 Mirzayev, S., & Khakimov, S. (2019). Statistical estimation of extreme water discharges in the Syrdarya and Amudarya rivers using probability distributions. Central Asian Journal of Water Research, 5(1), 45–58.
8 Tadayon, S. (2005). Water withdrawals for irrigation and water supply in the Amu Darya Basin. U.S. Geological Survey Scientific Investigations Report.
9 Min, A., & Czado, C. (2010). Bayesian inference for multivariate copulas using pair-copula constructions. Journal of Financial Econometrics, 8(4), 511–546.
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