Monitoring and Forecasting Land Subsidence in the Shahr-e Kord Plain Using Sentinel-1 Time Series and Deep Learning Models

چکیده مقاله

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 1 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

کلیدواژه‌ها

نویسندگان

Farhad LajmOwrak

PhD in Water Resources Engineering, Technical Expert, Shahr e Kord Municipality

Farshad Jafari

BSc in Civil Engineering, Architecture Expert, Shahr e Kord Municipality

Ashkan Amani Babadi

MSc in Structural Engineering, Urban Planning Expert, Shahr e Kord Municipality

Iman Khosravi Rastabi

MSc in Geotechnical Engineering, Urban Planning Expert, Shahr e Kord Municipality

Kianoosh Naderi Boldaji

Director General, Provincial Crisis Management Headquarters, Chaharmahal and Bakhtiari Province

Samieh Aalaei Boroujeni

Senior Expert in Crisis Affairs, Provincial Crisis Management Headquarters, Chaharmahal and Bakhtiari Province

LajmOwrak, Farhad and Jafari, Farshad and Amani Babadi, Ashkan and Khosravi Rastabi, Iman and Naderi Boldaji, Kianoosh and Aalaei Boroujeni, Samieh,1404,Monitoring and Forecasting Land Subsidence in the Shahr-e Kord Plain Using Sentinel-1 Time Series and Deep Learning Models,Third International Conference on applied researches in civil engineering, architecture and urban planning

ارائه‌شده در

مجموعه مقالات سومین کنفرانس بین المللی پژوهش های کاربردی در مهندسی عمران، معماری و شهرسازی30 آبان 1404