Evaluating the Impact of Land Subsidence on Power Transmission Infrastructure in the Tehran Plains: Insights from Multi-Source Data Integration and Predictive Modeling
Keywords:
land subsidence, power transmission towers, vulnerability assessment, remote sensing, hybrid AI-geotechnical modeling, Tehran Plains, predictive forecastingAbstract
Land subsidence threatens critical infrastructure, particularly power transmission towers, in rapidly urbanizing regions like the Tehran Plains, Iran. Building on the methodologies of Akbari Garakani et al. (2025), this study integrates multi-source datasets, including InSAR, geotechnical borings, and geophysical surveys, to assess tower vulnerability. A novel hybrid model combining finite element analysis (FEA) with random forest regression forecasts subsidence trends to 2030, revealing rates of 5-10 cm/year and potential structural stresses exceeding safe limits by 40-60%. Two tables compare regional subsidence and stress impacts, while figures map spatial patterns and temporal trends. The originality lies in the hybrid predictive framework, enhancing accuracy by 15% over traditional models. Recommendations include adaptive foundation designs to mitigate risks, contributing to sustainable infrastructure resilience in subsidence-prone areas.