Application of deep learning and GIS-based risk assessment for urban gas pipelines in geologically active regions: a case study of Almaty
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2025
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| _version_ | 1866901868793298944 |
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| author | Eginov, Aiaal Anatolevich |
| author_facet | Eginov, Aiaal Anatolevich |
| contents | <p><em><span lang="EN-US">Purpose. To develop a lightweight, modular method for assessing urban gas-pipeline vulnerability in seismically active areas, using Almaty as a pilot case. Methods. The approach couples deep</span><span lang="EN-US"> </span><span lang="EN-US">–</span><span lang="EN-US"> </span><span lang="EN-US">learning</span><span lang="EN-US"> </span><span lang="EN-US">–</span><span lang="EN-US"> </span><span lang="EN-US">based surface defect detection (a customized You Only Look Once model – YOLOv11-M) with GIS risk modeling built on a Digital Elevation Model (DEM), terrain slope, and active fault proximity. Detected defects are georeferenced and fused into a Composite Risk Index (CRI) within QGIS. Results. Validation indicates high recognition accuracy (mean Average Precision, mAP = 95.3%; F1-score = 95.2%) and a strong spatial correlation between geohazard zones and defect density, resulting in risk maps that prioritize segments for inspection and prevention. Significance. The framework is scalable and cost-effective for data-constrained municipalities, supporting urban resilience and Smart City strategies.</span></em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17152037 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Application of deep learning and GIS-based risk assessment for urban gas pipelines in geologically active regions: a case study of Almaty Eginov, Aiaal Anatolevich urban gas pipelines seismic hazard deep learning defect detection GIS-based modeling Digital Elevation Model Composite Risk Index <p><em><span lang="EN-US">Purpose. To develop a lightweight, modular method for assessing urban gas-pipeline vulnerability in seismically active areas, using Almaty as a pilot case. Methods. The approach couples deep</span><span lang="EN-US"> </span><span lang="EN-US">–</span><span lang="EN-US"> </span><span lang="EN-US">learning</span><span lang="EN-US"> </span><span lang="EN-US">–</span><span lang="EN-US"> </span><span lang="EN-US">based surface defect detection (a customized You Only Look Once model – YOLOv11-M) with GIS risk modeling built on a Digital Elevation Model (DEM), terrain slope, and active fault proximity. Detected defects are georeferenced and fused into a Composite Risk Index (CRI) within QGIS. Results. Validation indicates high recognition accuracy (mean Average Precision, mAP = 95.3%; F1-score = 95.2%) and a strong spatial correlation between geohazard zones and defect density, resulting in risk maps that prioritize segments for inspection and prevention. Significance. The framework is scalable and cost-effective for data-constrained municipalities, supporting urban resilience and Smart City strategies.</span></em></p> |
| title | Application of deep learning and GIS-based risk assessment for urban gas pipelines in geologically active regions: a case study of Almaty |
| topic | urban gas pipelines seismic hazard deep learning defect detection GIS-based modeling Digital Elevation Model Composite Risk Index |
| url | https://doi.org/10.5281/zenodo.17152037 |