Academic Journal
Machine learning-based ground motion simulation for seismic hazard assessment of critical water infrastructure in Azerbaijan (Case study: Major Shamkir water reservoir).
| Τίτλος: | Machine learning-based ground motion simulation for seismic hazard assessment of critical water infrastructure in Azerbaijan (Case study: Major Shamkir water reservoir). |
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| Συγγραφείς: | Babayev T; Department of Seismology and Seismic Hazard Assessment, Institute of Geology and Geophysics, Ministry of Science and Education, Baku, Azerbaijan., Babayev G; Department of Seismology and Seismic Hazard Assessment, Institute of Geology and Geophysics, Ministry of Science and Education, Baku, Azerbaijan., Bayramov E; Department of Geological Sciences, School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan., Irawan S; Department of Petroleum Engineering, School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan., Neafie J; Department of Political Science and International Relations, School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan., Aliyeva S; School of Agricultural and Food Sciences, ADA University, Baku, Azerbaijan. |
| Πηγή: | PloS one [PLoS One] 2026 Apr 01; Vol. 21 (4), pp. e0344984. Date of Electronic Publication: 2026 Apr 01 (Print Publication: 2026). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: San Francisco, CA : Public Library of Science |
| Ιατρικοί όροι (MeSH): | Machine Learning* , Earthquakes* , Water Supply*, Azerbaijan ; Neural Networks, Computer ; Random Forest ; Boosting Machine Learning Algorithms ; Predictive Learning Models ; Computer Simulation ; Prediction Algorithms ; Support Vector Machine |
| Περίληψη: | This study focuses on simulating ground motion for the Shamkir Water Reservoir area in Azerbaijan using machine learning algorithms to enhance regional seismic hazard assessments. Given the reservoir's location in a seismically active zone, potential earthquake-induced impacts on dam infrastructure pose critical safety concerns. A synthetic ground motion database comprising 3,013 records was developed using SeismoArtif software, utilizing earthquake magnitude, hypocentral distance, average shear-wave velocity (VS30) and site class as primary features. Four supervised machine learning models-Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost)-were developed to predict Peak Ground Acceleration (PGA). A dual-layered performance evaluation was conducted, comparing the models against each other and against the traditional A&K-1979 Ground Motion Prediction Equation (GMPE). Results demonstrate that while all machine learning models are highly applicable and physically consistent, the traditional GMPE fails significantly, yielding R2 of -6.87 and MAE of 284.88 Gals. Within the machine learning cohort, the Random Forest model achieved the highest training scores (R2: 0.8598), yet the Artificial Neural Network (ANN) emerged as the optimal architecture due to its superior generalization and stability. The ANN led the decisive testing phase with an R2 of 0.8437, an RMSE of 46.00 Gals, and the lowest systematic bias (-1.76 Gals) across all subsets. These findings underscore the robustness of data-driven approaches over fixed-coefficient empirical relationships, specifically highlighting the ANN's capability to provide unbiased, high-fidelity ground motion estimations for critical infrastructure risk-informed decision-making in Azerbaijan. (Copyright: © 2026 Babayev et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.) |
| Competing Interests: | The authors have declared that no competing interests exist. |
| Entry Date(s): | Date Created: 20260401 Date Completed: 20260714 Latest Revision: 20260714 |
| Update Code: | 20260714 |
| PubMed Central ID: | PMC13043061 |
| DOI: | 10.1371/journal.pone.0344984 |
| PMID: | 41920940 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0344984 |