Adaptive Warning Thresholds for Dam Safety: A KDE-Based Approach
Dams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Traditional monitoring systems typically employ predic...
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MDPI AG
2025-06-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/10/7/158 |
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| author | Nathalia Silva-Cancino Fernando Salazar Joaquín Irazábal Juan Mata |
| author_facet | Nathalia Silva-Cancino Fernando Salazar Joaquín Irazábal Juan Mata |
| author_sort | Nathalia Silva-Cancino |
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| description | Dams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Traditional monitoring systems typically employ predictive models—based on techniques such as the finite element method (FEM) or machine learning (ML)—to compare real-time data against expected performance. However, these models often rely on static warning thresholds, which fail to reflect the dynamic conditions affecting dam behavior, including fluctuating water levels, temperature variations, and extreme weather events. This study introduces an adaptive warning threshold methodology for dam safety based on kernel density estimation (KDE). The approach incorporates a boosted regression tree (BRT) model for predictive analysis, identifying influential variables such as reservoir levels and ambient temperatures. KDE is then used to estimate the density of historical data, allowing for dynamic calibration of warning thresholds. In regions of low data density—where prediction uncertainty is higher—the thresholds are widened to reduce false alarms, while in high-density regions, stricter thresholds are maintained to preserve sensitivity. The methodology was validated using data from an arch dam, demonstrating improved anomaly detection capabilities. It successfully reduced false positives in data-sparse conditions while maintaining high sensitivity to true anomalies in denser data regions. These results confirm that the proposed methodology successfully meets the goals of enhancing reliability and adaptability in dam safety monitoring. This adaptive framework offers a robust enhancement to dam safety monitoring systems, enabling more reliable detection of structural issues under variable operating conditions. |
| format | Article |
| id | doaj-art-dda671ca62fd411c992f9eade50ef92a |
| institution | Kabale University |
| issn | 2412-3811 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Infrastructures |
| spelling | doaj-art-dda671ca62fd411c992f9eade50ef92a2025-08-20T03:58:31ZengMDPI AGInfrastructures2412-38112025-06-0110715810.3390/infrastructures10070158Adaptive Warning Thresholds for Dam Safety: A KDE-Based ApproachNathalia Silva-Cancino0Fernando Salazar1Joaquín Irazábal2Juan Mata3Centre Internacional de Mètodes Numèrics en Enginyeria (CIMNE), Flumen Research Institute, Universitat Politècnica de Catalunya-BarcelonaTech (UPC), 08034 Barcelona, SpainCentre Internacional de Mètodes Numèrics en Enginyeria (CIMNE), Flumen Research Institute, Universitat Politècnica de Catalunya-BarcelonaTech (UPC), 08034 Barcelona, SpainCentre Internacional de Mètodes Numèrics en Enginyeria (CIMNE), Flumen Research Institute, Universitat Politècnica de Catalunya-BarcelonaTech (UPC), 08034 Barcelona, SpainNational Laboratory for Civil Engineering (LNEC), Concrte Dams Department, 1700-066 Lisboa, PortugalDams are critical infrastructures that provide essential services such as water supply, hydroelectric power generation, and flood control. As many dams age, the risk of structural failure increases, making safety assurance more urgent than ever. Traditional monitoring systems typically employ predictive models—based on techniques such as the finite element method (FEM) or machine learning (ML)—to compare real-time data against expected performance. However, these models often rely on static warning thresholds, which fail to reflect the dynamic conditions affecting dam behavior, including fluctuating water levels, temperature variations, and extreme weather events. This study introduces an adaptive warning threshold methodology for dam safety based on kernel density estimation (KDE). The approach incorporates a boosted regression tree (BRT) model for predictive analysis, identifying influential variables such as reservoir levels and ambient temperatures. KDE is then used to estimate the density of historical data, allowing for dynamic calibration of warning thresholds. In regions of low data density—where prediction uncertainty is higher—the thresholds are widened to reduce false alarms, while in high-density regions, stricter thresholds are maintained to preserve sensitivity. The methodology was validated using data from an arch dam, demonstrating improved anomaly detection capabilities. It successfully reduced false positives in data-sparse conditions while maintaining high sensitivity to true anomalies in denser data regions. These results confirm that the proposed methodology successfully meets the goals of enhancing reliability and adaptability in dam safety monitoring. This adaptive framework offers a robust enhancement to dam safety monitoring systems, enabling more reliable detection of structural issues under variable operating conditions.https://www.mdpi.com/2412-3811/10/7/158warning thresholdsdam safetykernel density estimation (KDE)anomaly detectionBoosted Regression Tree (BRT) |
| spellingShingle | Nathalia Silva-Cancino Fernando Salazar Joaquín Irazábal Juan Mata Adaptive Warning Thresholds for Dam Safety: A KDE-Based Approach Infrastructures warning thresholds dam safety kernel density estimation (KDE) anomaly detection Boosted Regression Tree (BRT) |
| title | Adaptive Warning Thresholds for Dam Safety: A KDE-Based Approach |
| title_full | Adaptive Warning Thresholds for Dam Safety: A KDE-Based Approach |
| title_fullStr | Adaptive Warning Thresholds for Dam Safety: A KDE-Based Approach |
| title_full_unstemmed | Adaptive Warning Thresholds for Dam Safety: A KDE-Based Approach |
| title_short | Adaptive Warning Thresholds for Dam Safety: A KDE-Based Approach |
| title_sort | adaptive warning thresholds for dam safety a kde based approach |
| topic | warning thresholds dam safety kernel density estimation (KDE) anomaly detection Boosted Regression Tree (BRT) |
| url | https://www.mdpi.com/2412-3811/10/7/158 |
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