Dergiler / The Eurasia Proceedings of Science Technology Engineering and Mathematics / 2026 / Volume: 34

A GIS and Drone-Based Risk Assessment Framework for Hydroelectric and Solar Energy Infrastructure in Disaster-Prone Areas with High Forestry and Agricultural Activity

Oyku Alkan(Alkan Harita Co.)
Mehmet Nurullah Alkan(Hitit University)
Sayfa
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Yayın Tarihi
2025-08-01
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The proposed framework provides a robust tool for risk-informed energy deployment while strengthening environmental resilience in disaster-prone regions.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The increasing global demand for renewable energy has accelerated the expansion of hydroelectric (HEPPs) and solar power plants (SEPs), frequently located in ecologically fragile and geologically hazardous areas.
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Temel Bulgular & Veri:This study proposes an integrated geospatial risk assessment framework combining Geographic Information Systems (GIS) and Unmanned Aerial Vehicles (UAVs) to evaluate and mitigate multi-hazard risks affecting renewable energy infrastructure.
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Sonuç & Katkı:The proposed framework provides a robust tool for risk-informed energy deployment while strengthening environmental resilience in disaster-prone regions.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Drones · Overlay analysis · Renewable energy risk assessment

Abstract

The increasing global demand for renewable energy has accelerated the expansion of hydroelectric (HEPPs) and solar power plants (SEPs), frequently located in ecologically fragile and geologically hazardous areas. This study proposes an integrated geospatial risk assessment framework combining Geographic Information Systems (GIS) and Unmanned Aerial Vehicles (UAVs) to evaluate and mitigate multi-hazard risks affecting renewable energy infrastructure. Focusing on Turkey's North-Middle Black Sea Region intersected by the seismically active North Anatolian Fault (NAF) and encompassing the geologically complex Obruk Dam basin this research addresses an area with dense renewable energy installations and high susceptibility to tectonic and climate-induced hazards. High-resolution UAV imagery, coupled with 3D terrain modeling, was utilized to assess infrastructure vulnerabilities. GIS-based spatial overlay techniques integrated multiple data layers, including active fault lines, slope instability, flood-prone zones, and land use classifications. Time-series change detection analyses were conducted to monitor landscape dynamics related to erosion, vegetation loss, and anthropogenic disturbances. A key component of the framework involves applying geomatics engineering principles, such as UAV-derived digital elevation model (DEM) validation and spatial dataset calibration.Using multi-criteria decision analysis (MCDA), critical risk hotspots particularly in the Obruk Dam basin were identified, highlighting infrastructure segments with heightened exposure to seismic and hydrological threats. Associated risks in surrounding agricultural and forested zones, such as wildfire vulnerability and soil deterioration, were further assessed. This study advances geomatics engineering by presenting a scalable, UAVGIS-integrated methodology for multi-dimensional risk modeling in renewable energy planning. The proposed framework provides a robust tool for risk-informed energy deployment while strengthening environmental resilience in disaster-prone regions.

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Bu Makaleye Atıf Yap

Alkan, O., Alkan, M. N. (2025). A GIS and Drone-Based Risk Assessment Framework for Hydroelectric and Solar Energy Infrastructure in Disaster-Prone Areas with High Forestry and Agricultural Activity. The Eurasia Proceedings of Science Technology Engineering and Mathematics, Volume: 34. https://doi.org/10.55549/epstem.1754579

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Kaynakça (1)

  1. Alkan, O., & Alkan, M.N. (2025). A GIS and drone-based risk assessment framework for hydroelectric and solar energy infrastructure in disaster-prone areas with high forestry and agricultural activity. The Eurasia Proceedings of Science, Technology, Engineering and Mathematics (EPSTEM), 34, 268-277.