Reconstruction of Groundwater Level Data Using Temporal Components of Groundwater Fluctuations Based on Wavelet Analysis and Artificial Neural Networks

Shevchenko, Oleksii; Charnyi, Dmytro; Zaslavsky, Ilya; Samalavičius, Vytautas; Sovkova, Yuliia

doi:10.3390/w18172105

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Reconstruction of Groundwater Level Data Using Temporal Components of Groundwater Fluctuations Based on Wavelet Analysis and Artificial Neural Networks

by

Oleksii Shevchenko

^1,*

,

Dmytro Charnyi

²

,

Ilya Zaslavsky

³

,

Vytautas Samalavičius

⁴

and

Yuliia Sovkova

¹

Ukrainian Hydrometeorological Institute NAS of Ukraine, Nauki Av., 37, 03028 Kyiv, Ukraine

²

Institute of Environmental Geochemistry NAS of Ukraine, Akademika Palladina Avenue, 34-a, 03142 Kyiv, Ukraine

³

Spatial Information Systems Laboratory, San Diego Supercomputer Center, University of California San Diego, 9836 Hopkins Drive, La Jolla, CA 92093, USA

⁴

Institute of Geosciences, Faculty of Chemistry and Geosciences at Vilnius University, Čiurlionio Str. 21/27, 03101 Vilnius, Lithuania

^*

Author to whom correspondence should be addressed.

Water 2026, 18(17), 2105; https://doi.org/10.3390/w18172105

Submission received: 8 May 2026 / Revised: 9 August 2026 / Accepted: 20 August 2026 / Published: 26 August 2026

(This article belongs to the Section Hydrogeology)

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Abstract

Since the observations of groundwater level (GWL) in Ukraine are not conducted by automated means, the regularity of the data is affected by the human factor as well as social unrest. Since 2022, this has been a full-scale war launched by the russian federation. Continuous long-term GWL observations (to 2011, sometimes until 2017) were used to reconstruct periods with missing measurements, combining autocorrelation analysis, wavelet decomposition, Mann–Kendall trend testing, and artificial neural networks (ANNs). The strongest reconstruction performance was achieved by separating GWL fluctuations into short-, medium-, and long-period components and modeling the dominant medium- and long-period structures. Compared with linear autoregressive baselines, multilayer perceptrons (MLPs) better approximated nonlinear relationships present in the historical record. At the same time, these data-driven models remain sensitive to nonstationarity and should be interpreted as predictive tools rather than causal process models. The data reconstruction study covers the transboundary basin of the Bug River, which is significant for Ukraine and Poland as a water resource.

Keywords: groundwater level; artificial neural networks; wavelet analysis; reconstruction; multilayer perceptrons; Western Bug basin

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MDPI and ACS Style

Shevchenko, O.; Charnyi, D.; Zaslavsky, I.; Samalavičius, V.; Sovkova, Y. Reconstruction of Groundwater Level Data Using Temporal Components of Groundwater Fluctuations Based on Wavelet Analysis and Artificial Neural Networks. Water 2026, 18, 2105. https://doi.org/10.3390/w18172105

AMA Style

Shevchenko O, Charnyi D, Zaslavsky I, Samalavičius V, Sovkova Y. Reconstruction of Groundwater Level Data Using Temporal Components of Groundwater Fluctuations Based on Wavelet Analysis and Artificial Neural Networks. Water. 2026; 18(17):2105. https://doi.org/10.3390/w18172105

Chicago/Turabian Style

Shevchenko, Oleksii, Dmytro Charnyi, Ilya Zaslavsky, Vytautas Samalavičius, and Yuliia Sovkova. 2026. "Reconstruction of Groundwater Level Data Using Temporal Components of Groundwater Fluctuations Based on Wavelet Analysis and Artificial Neural Networks" Water 18, no. 17: 2105. https://doi.org/10.3390/w18172105

APA Style

Shevchenko, O., Charnyi, D., Zaslavsky, I., Samalavičius, V., & Sovkova, Y. (2026). Reconstruction of Groundwater Level Data Using Temporal Components of Groundwater Fluctuations Based on Wavelet Analysis and Artificial Neural Networks. Water, 18(17), 2105. https://doi.org/10.3390/w18172105

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Reconstruction of Groundwater Level Data Using Temporal Components of Groundwater Fluctuations Based on Wavelet Analysis and Artificial Neural Networks

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