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Volume 17
Issue 21
10.3390/w17213148
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Research on Surface Water State for Rivers in Western Ukraine Using Time Series Forecasting Methods

by
Leonid Bytsyura
1,
Nina Szczepanik-Scislo
2,*,
Oksana Desyatnyuk
1,
Natalya Shakhovska
3,
Lukasz Scislo
4,
Anatoliy Sachenko
1,5,
Olena Lototska
6,
Ihor Shevchuk
3 and
Oksana Sofinska
7
1
West Ukrainian National University Ternopil, Lvivska str., 11, 46000 Ternopil, Ukraine
2
Faculty of Environmental Engineering and Energy, Cracow University of Technology, 24, Warszawska, 31-155 Cracow, Poland
3
Institute of Computer Sciences and Information Technology, Lviv Polytechnic National University, Stepana Bandery Street, 28A, 79000 Lviv, Ukraine
4
Faculty of Electrical and Computer Engineering, Cracow University of Technology, 24, Warszawska, 31-155 Cracow, Poland
5
Department of Teleinformatics, Casimir Pulaski Radom University, 29, Malczewskiego Street, 26-600 Radom, Poland
6
Faculty of medicine, I.Horbachevsky Ternopil National Medical University, 1 Maidan Voli, 46001 Ternopil, Ukraine
7
Ternopil Regional Centre for Hydrometeorology, Novyi Svit St, 25, 46003 Ternopil, Ukraine
*
Author to whom correspondence should be addressed.
Water 2025, 17(21), 3148; https://doi.org/10.3390/w17213148
Submission received: 19 June 2025 / Revised: 1 September 2025 / Accepted: 29 October 2025 / Published: 2 November 2025
(This article belongs to the Special Issue Water Quality Monitoring and Prediction Using New Sensors, Machine Learning and Big Data)
Versions Notes

Abstract

This study presents a data-driven forecasting framework for surface water state trends using time-series modelling based on hydrochemical monitoring data from the Ikva River (Ukraine). The monitoring campaign, conducted between 2021 and 2023, involved monthly sampling of 19 hydrochemical indicators at two sites. We applied the Prophet time series forecasting algorithm, a decomposable additive model, to predict key indicators, including water hardness and bicarbonate concentration. The approach provides a transparent and adaptable method for forecasting water state in data-limited contexts. Key contributions include the integration of high-resolution hydrochemical monitoring with an explainable machine learning model, enabling early warning insights in under-monitored river basins. The case study of best-performing models for hydrocarbonate and hardness confirmed that Prophet offered well-calibrated prediction intervals with rapid deployment, high interpretability, and dependable uncertainty estimation, though its forecasts were comparatively less accurate. Analysis of computational performance shows that Prophet enables faster implementation and quick insights, while ARIMA and LSTM achieve higher predictive accuracy at the cost of longer execution times. Results demonstrate strong predictive skill: for hardness, MAE = 1.64 and RMSE = 1.73; for bicarbonate, MAE = 54.82 and RMSE = 62.00. Coverage accuracy of 95% prediction intervals exceeded 91% for both indicators. The proposed approach provides a practical foundation for implementing early-warning systems and supporting evidence-based water resource management in regions lacking real-time monitoring infrastructure.
Keywords: environmental monitoring; sensor technology; forecasting algorithm; surface water; machine learning; artificial intelligence; modelling; correlation; time series processing environmental monitoring; sensor technology; forecasting algorithm; surface water; machine learning; artificial intelligence; modelling; correlation; time series processing

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MDPI and ACS Style

Bytsyura, L.; Szczepanik-Scislo, N.; Desyatnyuk, O.; Shakhovska, N.; Scislo, L.; Sachenko, A.; Lototska, O.; Shevchuk, I.; Sofinska, O. Research on Surface Water State for Rivers in Western Ukraine Using Time Series Forecasting Methods. Water 2025, 17, 3148. https://doi.org/10.3390/w17213148

AMA Style

Bytsyura L, Szczepanik-Scislo N, Desyatnyuk O, Shakhovska N, Scislo L, Sachenko A, Lototska O, Shevchuk I, Sofinska O. Research on Surface Water State for Rivers in Western Ukraine Using Time Series Forecasting Methods. Water. 2025; 17(21):3148. https://doi.org/10.3390/w17213148

Chicago/Turabian Style

Bytsyura, Leonid, Nina Szczepanik-Scislo, Oksana Desyatnyuk, Natalya Shakhovska, Lukasz Scislo, Anatoliy Sachenko, Olena Lototska, Ihor Shevchuk, and Oksana Sofinska. 2025. "Research on Surface Water State for Rivers in Western Ukraine Using Time Series Forecasting Methods" Water 17, no. 21: 3148. https://doi.org/10.3390/w17213148

APA Style

Bytsyura, L., Szczepanik-Scislo, N., Desyatnyuk, O., Shakhovska, N., Scislo, L., Sachenko, A., Lototska, O., Shevchuk, I., & Sofinska, O. (2025). Research on Surface Water State for Rivers in Western Ukraine Using Time Series Forecasting Methods. Water, 17(21), 3148. https://doi.org/10.3390/w17213148

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