Monitoring and Forecasting Land Subsidence in the Shahr-e Kord Plain Using Sentinel-۱ Time Series and Deep Learning Models

Land subsidence, as a gradual and infrastructure-related hazard, has intensified in recent decades across many plains of Iran, including the Shahr-e Kord Plain. The main drivers of this phenomenon include declining groundwater levels, compressible fine-grained soils, and uncontrolled urban expansion. Given the destructive impacts of subsidence on urban infrastructure, agriculture, and natural resources, precise monitoring, scientific forecasting, and managerial intervention are urgently needed at the provincial level in Chaharmahal and Bakhtiari. This study aims to design an operational framework for monitoring and forecasting land subsidence in the Shahr-e Kord Plain using Sentinel-۱ time series data, the SBAS-InSAR algorithm, and deep learning models. The proposed framework is intended to support decision-making processes within the Provincial Crisis Management Headquarters, the Regional Water Company, the Department of Natural Resources, and the Shahr-e Kord Municipality.

کلیدواژه ها:

Shahr-e Kord Plain ، Land Subsidence ، Hazards ، Sentinel-۱ ، SBAS-InSAR