Volume 8, Issue 2 (2020)                   ECOPERSIA 2020, 8(2): 77-87 | Back to browse issues page

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Chatrsimab Z, Alesheikh A, Vosoghi B, Behzadi S, Modiri M. Land Subsidence Modelling Using Particle Swarm Optimization Algorithm and Differential Interferometry Synthetic Aperture Radar. ECOPERSIA 2020; 8 (2) :77-87
URL: http://ecopersia.modares.ac.ir/article-24-35574-en.html
1- Department of GIS/RS, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- GIS Engineering Department, K.N. Toosi University of Technology, Tehran, Iran , alesheikh@kntu.ac.ir
3- Geodesy Department, K.N. Toosi University of Technology, Tehran, Iran
4- Civil Engineering Department, Shahid Rajaee Teacher Training University, Tehran, Iran
5- Geography Urban Panning Department, Malek Ashtar University of Technology, Tehran, Iran
Abstract:   (2695 Views)
Aims: Land subsidence is one of the phenomena that has been abundantly observed in Iran's fertile plains in recent decades. If it is not properly managed, it will cause irreparable damages. So, regarding the frequency of subsidence phenomenon, the evaluation of the potential of the country's fertile plains is necessary. Towards this, the present study is formulated to assess the vulnerability of the Tehran-Karaj-Shahriyar Aquifer to land subsidence.
Materials & Methods: The vulnerability of Tehran-Karaj-Shahriyar Aquifer was determined using the GARDLIF method in a Geographic Information System (GIS) environment. Seven parameters affecting ground subsidence including groundwater loss, aquifer media, recharge, discharge, land use, aquifer layer thickness, and the fault distance were used to identify areas susceptible to land subsidence. Then, they were ranked and weighted in seven separate layers. In the next step, the subsidence location and rates were obtained using the differential interferometric synthetic aperture radar (DInSAR) method. The weights of the input parameters of the GARDLIF model using the subsidence map obtained from the DInSAR method and the particle optimization algorithm (PSO) were then optimized. Accordingly, the subsidence susceptibility map was generated based on the new weights.
Findings & Conclusion: The results showed that by increasing correlation coefficient (r) from 0.55 to 0.67 and the amounts of Coefficient of Determination (R2) from 0.39 to 0.53 between the subsidence index and the obtained subsidence in the aquifer, the optimization of weights applied by the PSO algorithm is more capable for evaluating the land subsidence than the map created by GARDLIF. It was also found that the central parts of the study aquifer had the largest potential for land subsidence.
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Article Type: Letter to Editor | Subject: Ecosystem Management, Monitoring, Policy and Law
Received: 2019/08/10 | Accepted: 2019/12/8 | Published: 2020/05/19
* Corresponding Author Address: No. 1346, ValiAsr Avenue., Mirdamad Street, Tehran, Iran. Postal Code: 1996715433.

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