Mathematical forecasting of spatio-temporal dynamics of hydroecological parameters of river ecosystems using integrally-modified Streeter-Phelps model

Tsyhanenko-Dziubenko, I.; Kireitseva, H.; Sheliah, K.; Levytska, T.; Kalenska, V.

dc.contributor.author	Tsyhanenko-Dziubenko, I.
dc.contributor.author	Kireitseva, H.
dc.contributor.author	Sheliah, K.
dc.contributor.author	Levytska, T.
dc.contributor.author	Kalenska, V.
dc.date.accessioned	2025-10-10T10:24:06Z
dc.date.available	2025-10-10T10:24:06Z
dc.date.issued	2025
dc.identifier.uri	https://eztuir.ztu.edu.ua/123456789/8915
dc.description	Tsyhanenko-Dziubenko, I., Kireitseva, H., Sheliah, K., Levytska, T., Kalenska, V. (2025). Mathematical forecasting of spatio-temporal dynamics of hydroecological parameters of river ecosystems using integrally-modified Streeter-Phelps model. Journal Environmental Problems, 10(3), 309–318. DOI: https://doi.org/10.23939/ep2025.03.309	uk_UA
dc.language.iso	en	uk_UA
dc.relation.ispartofseries	Journal Environmental Problems;№10(3)
dc.subject	Streeter-Phelps model	uk_UA
dc.subject	hydroecological forecasting	uk_UA
dc.subject	urban river systems	uk_UA
dc.subject	mathematical modeling	uk_UA
dc.subject	water quality prediction	uk_UA
dc.title	Mathematical forecasting of spatio-temporal dynamics of hydroecological parameters of river ecosystems using integrally-modified Streeter-Phelps model	uk_UA
dc.type	Article	uk_UA
dc.description.abstracten	This study presents a comprehensive mathematical forecasting approach for hydroecological parameters in small urban river systems using an integrally- modified Streeter-Phelps model. The research focuses on the Kamyanka River, a small tributary within Zhytomyr city, Ukraine, which experiences significant anthropogenic influence from urban development. The modified model incorporates advanced computational algorithms implemented in Python programming environment to predict dissolved oxygen concentration and biochemical oxygen demand dynamics over a 25-year period (2020–2045). Model verification using observational data from 2020–2023 demonstrated high accuracy with R² = 0.87 and root mean square deviation of ±0.2 mg/L for dissolved oxygen predictions. The results reveal a positive trend in oxygen regime optimization, with dissolved oxygen concentrations projected to increase from 8.5 mg/L to 11.0 mg/L, while biochemical oxygen demand is expected to decrease from 4.0 to 3.0 mg O2/L. Statistical analysis confirmed model reliability through Nash-Sutcliffe efficiency coefficient (NSE = 0.84) and cross-validation metrics (R²ᶜᵛ = 0.83). The developed forecasting system provides robust framework for environmental management and supports long-term planning strategies for ecological rehabilitation of urbanized river ecosystems.	uk_UA