THE COMBINED USE OF LONG-TERM MULTI-SENSOR INSAR ANALYSIS AND FINITE ELEMENT SIMULATION TO PREDICT LAND SUBSIDENCE

Gharehdaghi, M.; Fakher, A.; Cheshomi, A.

Land subsidence in Tehran Plain, Iran, for the period of 2003–2017 was measured using an InSAR time series investigation of surface displacements. In the presented study, land subsidence in the southwest of Tehran is characterized using InSAR data and numerical modelling, and the trend is predicted through future years. Over extraction of groundwater is the most common reason for the land subsidence which may cause devastating consequences for structures and infrastructures such as demolition of agricultural lands, damage from a differential settlement, flooding, or ground fractures. The environmental and economic impacts of land subsidence emphasize the importance of modelling and prediction of the trend of it in order to conduct crisis management plans to prevent its deleterious effects. In this study, land subsidence caused by the withdrawal of groundwater is modelled using finite element method software Plaxis 2D. Then, the model was verified using InSAR data. The results were in good agreement with the measurement results. The calibrated model was used to predict the land subsidence in future years. It could predict future subsidence for any assumed rate of water depletion.

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Gharehdaghi, M. / Fakher, A. / Cheshomi, A.: THE COMBINED USE OF LONG-TERM MULTI-SENSOR INSAR ANALYSIS AND FINITE ELEMENT SIMULATION TO PREDICT LAND SUBSIDENCE. 2019. Copernicus Publications.

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Rechteinhaber: M. Gharehdaghi et al.

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