Modeling Ecological Risk in Bottom Sediments Using Predictive Data Analytics: Implications for Energy Systems

Bartosz Przysucha, Monika Kulisz, Justyna Kujawska (), Michał Cioch, Adam Gawryluk and Rafał Garbacz
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Bartosz Przysucha: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Monika Kulisz: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Justyna Kujawska: Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland
Michał Cioch: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Adam Gawryluk: Faculty of Agrobioengineering, University of Life Sciences in Lublin, 20-950 Lublin, Poland
Rafał Garbacz: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland

Energies, 2025, vol. 18, issue 9, 1-23

Abstract: Sediment accumulation in dam reservoirs significantly impacts hydropower efficiency and infrastructure sustainability. Bottom sediments often contain heavy metals such as Cr, Ni, Cu, Zn, Cd, and Pb, which can pose ecological risks and affect water quality. Moreover, excessive sedimentation reduces reservoir capacity, increases turbine wear, and raises operational costs, ultimately hindering energy production. This study examined the ecological risk of heavy metals in bottom sediments and explored predictive approaches to support sediment management. Using 27 sediment samples from Zemborzyce Lake, the concentrations of selected heavy metals were measured at two depths (5 cm and 30 cm). Ecological risk index (ERI) values for the deep layer were predicted based on surface data using artificial neural networks (ANNs) and multiple linear regression (MLR). Both models showed a high predictive accuracy, demonstrating the potential of data-driven methods in sediment quality assessment. The early identification of high-risk areas allows for targeted dredging and optimized maintenance planning, minimizing disruption to dam operations. Integrating predictive analytics into hydropower management enhances system resilience, environmental protection, and long-term energy efficiency.

Keywords: ecological risk index (ERI); bottom sediments; heavy metals; artificial neural network (ANN); environmental monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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