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:   (2437 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.

1. Motiee H, McBean E. Assessment of climate change impacts on groundwater recharge for different soil types-guelph region in grand river Basin, Canada. Ecopersia. 2017;5(2):1731-44. [Link]
2. Alijani R, Vafakhah M, Malekian A. Spatial and temporal analysis of monthly stream flow deficit intensity in Gorganroud watershed, Iran. Ecopersia. 2016;4(1):1313-29. [Link] [DOI:10.18869/modares.ecopersia.4.1.1313]
3. Motagh M, Walter TR, Sharifi MA, Fielding E, Schenk A, Anderssohn J, et al. Land subsidence in Iran caused by widespread water reservoir overexploitation. Geophys Res Lett. 2008;35(16):L16403. [Link] [DOI:10.1029/2008GL033814]
4. Bazrafshan O, Zamani H, Etedali HR, Dehghanpir S. Assessment of citrus water footprint components and impact of climatic and non-climatic factors on them. Sci Hortic. 2019;250:344-51. [Link] [DOI:10.1016/j.scienta.2019.02.069]
5. Bazrafshan O, Parandin F, Farokhzadeh B. Assessment of hydro-meteorological drought effects on groundwater resources in Hormozgan region-South of Iran. Ecopersia. 2016;4(4):1569-84.‏ [Link] [DOI:10.18869/modares.ecopersia.4.4.1569]
6. Hu R, Wang S, Lee C, Li M. Characteristics and trends of land subsidence in Tanggu, Tianjin, China. Bull Eng Geol Environ. 2002;61(3):213-25. [Link] [DOI:10.1007/s100640100132]
7. Bouwer H. Groundwater hydrology. New York: McGraw Hill College; 1978. [Link]
8. Maleki A, Rezaei P. Forecast locations at risk of subsidence plain Kermanshah. Modarres Hum Sci. 2016;20(1):235-51. [Persian] [Link]
9. Kuehn F, Albiol D, Cooksley G, Duro J, Granda J, Haas S, et al. Detection of land subsidence in Semarang, Indonesia, using stable points network (SPN) technique. Environ Earth Sci. 2010;60(5):909-21. [Link] [DOI:10.1007/s12665-009-0227-x]
10. Taheri Tizro A, Hosseini A, Kamali M. Modeling alluvial aquifer using PMWIN software and evaluation of subsidence phenomenon in Asadabad plain, Hamedan Province, Iran. Nat Environ Hazards. 2018;7(17):121-36. [Persian] [Link] [DOI:10.1007/978-3-319-77122-9_9]
11. Hazbavi Z, Sadeghi SH. Watershed health characterization using reliability-resilience-vulnerability conceptual framework based on hydrological responses. Land Degrad Dev. 2017;28(5):1528-37. [Link] [DOI:10.1002/ldr.2680]
12. Bhattarai R, Alifu H, Maitiniyazi A, Kondoh A. Detection of land subsidence in Kathmandu Valley, Nepal, using DInSAR technique. Land. 2017;6(2):39. [Link] [DOI:10.3390/land6020039]
13. Fulton A. Land subsidence: What is it and why is it an important aspects of groundwater management?. Sacramento: California Department of Water Resources; 2006. [Link]
14. Kim K, Lee S, Oh HJ. Prediction of ground subsidence in Samcheok City, Korea using artificial neural networks and GIS. Environ Geol. 2009;58(1):61-70. [Link] [DOI:10.1007/s00254-008-1492-9]
15. Oh HJ, Lee S. Assessment of ground subsidence using GIS and the weights-of-evidence model. Eng Geol. 2010;115(1-2):36-48. [Link] [DOI:10.1016/j.enggeo.2010.06.015]
16. Putra DP, Setianto A, Keokhampui K, Fukuoka H. Land subsidence risk assessment case study: Rongkop, Gunung Kidul, Yogyakarta-Indonesia. The 4th AUN/SEED‐Net Regional Conference on Geo‐Disaster Mitigation in ASEA, 2011 October 25-26, Thailand. Unknown Publisher city; Unknown Publisher; 2011. [Link]
17. Xu YS, Yuan Y, Shen SL, Yin ZY, Wu HN, Ma L. Investigation into subsidence hazards due to groundwater pumping from Aquifer II in Changzhou, China. Nat Hazard. 2015;78(1):281-96. [Link] [DOI:10.1007/s11069-015-1714-x]
18. Afifi MA. Assess the potential of land subsidence and its related factors (Case study: Plain Saidan Farouk MARVDASHT). Quant Geomorphol Res. 2017;5(3):121-32. [Persian] [Link]
19. Nadiri AA, Taheri Z, Khatibi R, Barzegari G, Dideban Kh. Introducing a new framework for mapping subsidence vulnerability indices (SVIs): ALPRIFT. Sci Total Environ. 2018;628:1043-57. [Link] [DOI:10.1016/j.scitotenv.2018.02.031]
20. Manafiazar A, Khamehchiyan M, Nadiri A. Comparison of vulnerability of the southwest Tehran plain aquifer with simple weighting model (ALPRIFT Model) and genetic algorithm (GA). Kharazmi J Earth Sci. 2019;4(2):199-212. [Persian] [Link]
21. Manafiazar A, Khamechian M, Nadiri A. Optimization of the ALPRIFT method using a support vector machine (SVM) to assess the subsidence Vulnerability of the southwestern plain of Tehran. J Eng Geol. 2018;11(2):1-14.[Persian] [Link]
22. Naderi K, Nadiri AA, Asghari Moghaddam A, Kord M. A new approach to determine probable land subsidence areas (Case study: The Salmas plain aquifer). Iran J Ecohydrol. 2018;5(1):85-97. [Persian] [Link`]
23. Bouwer H. Groundwater hydrology. Lotfi-Sadigh A, translator. Tabriz: Sahand University of Technology Press; 1995. [Persian]. [Link]
24. Galloway DL, Burbey TJ. Review: Regional land subsidence accompanying groundwater extraction. Hydrogeol J. 2011;19(8):1459-86. [Link] [DOI:10.1007/s10040-011-0775-5]
25. Pacheco J, Arzate J, Rojas E, Arroyo M, Yutsis V, Ochoa G. Delimitation of ground failure zones due to land subsidence using gravity data and finite element modeling in the Queretaro valley, Mexico. Eng Geol. 2006;84(3-4):143-60. [Link] [DOI:10.1016/j.enggeo.2005.12.003]
26. Alizadeh A. Principles of applied hydrology. 9th Edition. Mashhad: Imam Reza university Press; 1996. [Persian]. [Link]
27. Piscopo G. Groundwater vulnerability map explanatory notes. Parramatta: NSW Department of Land and Water Conservation; 2001. [Link]
28. Hafezimoghadas N, Ghafoori M. Enviromental Geology. 1st Edition. Shahrood: Shahrood University of Technology Press; 2009. [Persian]. [Link]
29. Kennedy J, Eberhart RC. Particle swarm optimization. Proceedings of ICNN'95-International Conference on Neural Networks, 1995 27 November-1 December, Perth, Australia. Piscataway: IEEE; 1995. [Link]
30. Enshaee A, Hooshmand R. Application of fuzzy particle swarm optimization in detection and classification of single and combined power quality disturbances. Modares J Electric Eng. 2010;10(2):1-16. [Persian] [Link]
31. Moghaddasi M, Morid S, Araghinejad Sh. Optimization of water allocation during water scarcity condition using non-linear programming, genetic algorithm and particle swarm optimization (case study). J Water Res Agric. 2009;4(3):1-13. [Persian] [Link]
32. Taherifar A, Alasty A, Salarieh H, Boroushaki M. Path planning for a planar hyper-redundant manipulator with lockable joints using particle swarm optimization. Modares Mech Eng. 2011;11(2):159-75. [Persian] [Link]
33. Shafiei Alavijeh M, Amirabadi H. Modeling and optimizing lapping process of 440C steel by neural network and multi-objective particle swarm optimization algorithm. Modares Mech Eng. 2017;17(8):201-12. [Persian] [Link]
34. Alizamir M, Azhdary Moghadam M, Hashemi Monfared A, Shamsipour A. A hybrid artificial neural network and particle swarm optimization algorithm for statistical downscaling of precipitation in arid region. Ecopersia. 2017;5(4):1991-2006. [Link]
35. Zebker HA, Goldstein RM. Topographic mapping from interferometric synthetic aperture radar observations. J Geophys Res. 1986;91(B5):4993-9. [Link] [DOI:10.1029/JB091iB05p04993]
36. Ahmad W, Choi M, Kim S, Kim D. Detection of land subsidence due to excessive groundwater use varying with different land cover types in Quetta valley, Pakistan using ESA-sentinel satellite data. Nat Hazards Earth Syst Sci Discusss. 2017;1-21. [Link] [DOI:10.5194/nhess-2017-234]
37. Caló F, Notti D, Galve JP, Abdikan S, Görüm T, Pepe A, et al. Dinsar-Based detection of land subsidence and correlation with groundwater depletion in Konya Plain, Turkey. Remote Sens. 2017;9(1):83. [] [DOI:10.3390/rs9010083]
38. Rahmati O, Falah F, Naghibi SA, Biggs T, Soltani M, Deo RC, et al. Land subsidence modelling using tree-based machine learning algorithms. Sci Total Environ. 2019;672:239-52. [Link] [DOI:10.1016/j.scitotenv.2019.03.496]
39. Razmgir R, Mousavi M, Shemshaki A, Bolourchi MJ. Tehran-Shahriar Plain subsidence due to excess extraction of underground water, preliminary survey. 1st National Conference on Coastal Water Resources Management, 2010 December 8, Sari, Iran. Sari: Sari University of Agricultural Sciences and Natural Resources; 2010. [Persian] [Link]
40. Safari A, Jafari F, Tavakoli Sabour S. Monitoring its land subsidence and its relation to groundwater harvesting case study: Karaj plain-Shahriar. Quant Geomorphol Res. 2016;5(2):82-93. [Persian] [Link]

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