Modeling Ecological Risk in Bottom Sediments Using Predictive Data Analytics: Implications for Energy Systems
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 r...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/9/2329 |
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| author | Bartosz Przysucha Monika Kulisz Justyna Kujawska Michał Cioch Adam Gawryluk Rafał Garbacz |
| author_facet | Bartosz Przysucha Monika Kulisz Justyna Kujawska Michał Cioch Adam Gawryluk Rafał Garbacz |
| author_sort | Bartosz Przysucha |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4f614a1e870149548f557c65e4e4ad79 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-4f614a1e870149548f557c65e4e4ad792025-08-20T02:58:47ZengMDPI AGEnergies1996-10732025-05-01189232910.3390/en18092329Modeling Ecological Risk in Bottom Sediments Using Predictive Data Analytics: Implications for Energy SystemsBartosz Przysucha0Monika Kulisz1Justyna Kujawska2Michał Cioch3Adam Gawryluk4Rafał Garbacz5Faculty of Management, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Management, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Management, Lublin University of Technology, 20-618 Lublin, PolandFaculty of Agrobioengineering, University of Life Sciences in Lublin, 20-950 Lublin, PolandFaculty of Management, Lublin University of Technology, 20-618 Lublin, PolandSediment 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.https://www.mdpi.com/1996-1073/18/9/2329ecological risk index (ERI)bottom sedimentsheavy metalsartificial neural network (ANN)environmental monitoring |
| spellingShingle | Bartosz Przysucha Monika Kulisz Justyna Kujawska Michał Cioch Adam Gawryluk Rafał Garbacz Modeling Ecological Risk in Bottom Sediments Using Predictive Data Analytics: Implications for Energy Systems Energies ecological risk index (ERI) bottom sediments heavy metals artificial neural network (ANN) environmental monitoring |
| title | Modeling Ecological Risk in Bottom Sediments Using Predictive Data Analytics: Implications for Energy Systems |
| title_full | Modeling Ecological Risk in Bottom Sediments Using Predictive Data Analytics: Implications for Energy Systems |
| title_fullStr | Modeling Ecological Risk in Bottom Sediments Using Predictive Data Analytics: Implications for Energy Systems |
| title_full_unstemmed | Modeling Ecological Risk in Bottom Sediments Using Predictive Data Analytics: Implications for Energy Systems |
| title_short | Modeling Ecological Risk in Bottom Sediments Using Predictive Data Analytics: Implications for Energy Systems |
| title_sort | modeling ecological risk in bottom sediments using predictive data analytics implications for energy systems |
| topic | ecological risk index (ERI) bottom sediments heavy metals artificial neural network (ANN) environmental monitoring |
| url | https://www.mdpi.com/1996-1073/18/9/2329 |
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