[صفحه اصلی ]   [Archive] [ English ]  
:: صفحه اصلي :: درباره نشريه :: آخرين شماره :: تمام شماره‌ها :: جستجو :: ثبت نام :: ارسال مقاله :: تماس با ما ::
:: دوره 7، شماره 3 - ( 9-1398 ) ::
جلد 7 شماره 3 صفحات 115-135 برگشت به فهرست نسخه ها
توسعه مدل پیش بینی گسترش فیزیکی شهر بابل مبتنی بر مفهوم درجه ریسک تصمیم‌گیری مکانی چندمعیاره
محمد کریمی فیروزجائی، امیر صدیقی، محمدرضا جلوخانی نیارکی
دانشگاه تهران
چکیده:   (170 مشاهده)
امروزه بررسی روند مکانی-زمانی گسترش فیزیکی شهرها و شناسایی پارامترهای موثر بر آن نقش کلیدی را در فرایند تصمیم­گیری­ها و برنامه­ریزی­های بلند مدت شهری ایفا می­کنند. بنابراین، بکارگیری روش­های دقیق و کارآمد برای پیش­بینی گسترش فیزیکی شهرها از اهمیت بالایی برخوردار است. هدف از این پژوهش، ارائه یک مدل مفهومی جدید برای پیاده­سازی سیستم پیش­بینی گسترش فیزیکی شهر برمبنای درجه ریسک در تصمیم­گیری مکانی چند معیاره می­باشد. این مدل برای پیش­بینی گسترش فیزیکی شهر بابل پیاده­سازی شده است. در مدل پیشنهادی از تلفیق روش وزن­دهی ذهنی و عینی به صورت سراسری و محلی، برای تعیین اهمیت نسبی معیارهای مختلف و از مدل مارکوف برای تولید ماتریس تبدیل مساحت استفاده شده است. علاوه بر دو پارامتر مقدار معیارها و وزن هر یک از معیارها، پارامتر درجه ریسک در تصمیم­گیری نیز برای تهیه نقشه تناسب گسترش فیزیکی شهر در نظر گرفته شده است. در این مطالعه برای تعیین درجه ریسک و روش­ وزن‌دهی بهینه، هر یک از نقشه­های اراضی ساخته­شده شبیه­سازی شده با نقشه اراضی ساخته­شده واقعی مقایسه شده است. نتایج بیانگر این است که برای منطقه مورد مطالعه در حالت تعیین وزن معیارها با استراتژی محلی و سراسری، مقادیر 3/0 و 7/0 به ترتیب درجات ریسک بهینه در تصمیم­گیری جهت تولید نقشه تناسب می­باشند. همچنین در درجه ریسک بهینه دقت کلی برای روش تعیین وزن سراسری و محلی به ترتیب 5/85 و 2/89 می­باشد. برای شهر بابل نتایج پژوهش حاکی از کارایی بالاتر روش وزن­دهی محلی نسبت به روش وزن­دهی سراسری برای تولید نقشه تناسب می­باشد.
واژه‌های کلیدی: گسترش فیزیکی شهر، مدل شبیه سازی، تصمیم‌گیری مکانی چند معیاره، ریسک.
متن کامل [PDF 2029 kb]   (78 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: سنجش از دور
دریافت: ۱۳۹۷/۵/۲۹ | پذیرش: ۱۳۹۷/۹/۲۰ | انتشار: ۱۳۹۸/۹/۳۰
فهرست منابع
1. [1] M. Batty, E. Besussi, and N. Chin, "Traffic, urban growth and suburban sprawl," 2003.
2. [2] B. Bhatta, S. Saraswati, and D. Bandyopadhyay, "Urban sprawl measurement from remote sensing data," Applied geography, vol. 30, no. 4, pp. 731-740, 2010. [DOI:10.1016/j.apgeog.2010.02.002]
3. [3] B. Mirbagheri and A. Alimohammadi, "Improving urban cellular automata performance by integrating global and geographically weighted logistic regression models," Transactions in GIS, vol. 21, no. 6, pp. 1280-1297, 2017. [DOI:10.1111/tgis.12278]
4. [4] K. Samir and W. Lutz, "The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100," Global Environmental Change, vol. 42, pp. 181-192, 2017. [DOI:10.1016/j.gloenvcha.2014.06.004]
5. [5] I. B. I. Malik and B. J. Dewancker, "Identification of Population Growth and Distribution, Based on Urban Zone Functions," Sustainability, vol. 10, no. 4, p. 930, 2018. [DOI:10.3390/su10040930]
6. [6] K. L. Findell et al., "The impact of anthropogenic land use and land cover change on regional climate extremes," Nature communications, vol. 8, no. 1, p. 989, 2017. [DOI:10.1038/s41467-017-01038-w]
7. [7] Y. Feng and X. Tong, "Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules," GIScience & Remote Sensing, pp. 1-21, 2018. [DOI:10.1080/15481603.2018.1426262]
8. [8] H. Zhang, Y. Zeng, L. Bian, and X. Yu, "Modelling urban expansion using a multi agent-based model in the city of Changsha," Journal of Geographical Sciences, vol. 20, no. 4, pp. 540-556, 2010. [DOI:10.1007/s11442-010-0540-z]
9. [9] R. White, I. Uljee, and G. Engelen, "Integrated modelling of population, employment and land-use change with a multiple activity-based variable grid cellular automaton," International Journal of Geographical Information Science, vol. 26, no. 7, pp. 1251-1280, 2012. [DOI:10.1080/13658816.2011.635146]
10. [10] R. Rafiee, A. S. Mahiny, N. Khorasani, A. A. Darvishsefat, and A. Danekar, "Simulating urban growth in Mashad City, Iran through the SLEUTH model (UGM)," Cities, vol. 26, no. 1, pp. 19-26, 2009. [DOI:10.1016/j.cities.2008.11.005]
11. [11] X. Liu, L. Ma, X. Li, B. Ai, S. Li, and Z. He, "Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata," International Journal of Geographical Information Science, vol. 28, no. 1, pp. 148-163, 2014. [DOI:10.1080/13658816.2013.831097]
12. [12] E. A. Silva and K. C. Clarke, "Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal," Computers, environment and urban systems, vol. 26, no. 6, pp. 525-552, 2002. [DOI:10.1016/S0198-9715(01)00014-X]
13. [13] L. Hua, L. Tang, S. Cui, and K. Yin, "Simulating urban growth using the Sleuth Model in a coastal peri-urban district in China," Sustainability, vol. 6, no. 6, pp. 3899-3914, 2014. [DOI:10.3390/su6063899]
14. [14] M. Wolff, D. Haase, and A. Haase, "Compact or spread? A quantitative spatial model of urban areas in Europe since 1990," PloS one, vol. 13, no. 2, p. e0192326, 2018. [DOI:10.1371/journal.pone.0192326]
15. [15] A. Mustafa, A. Heppenstall, H. Omrani, I. Saadi, M. Cools, and J. Teller, "Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm," Computers, Environment and Urban Systems, vol. 67, pp. 147-156, 2018. [DOI:10.1016/j.compenvurbsys.2017.09.009]
16. [16] B. C. Pijanowski, D. G. Brown, B. A. Shellito, and G. A. Manik, "Using neural networks and GIS to forecast land use changes: a land transformation model," Computers, environment and urban systems, vol. 26, no. 6, pp. 553-575, 2002. [DOI:10.1016/S0198-9715(01)00015-1]
17. [17] I. Benenson, "Multi-agent simulations of residential dynamics in the city," Computers, Environment and Urban Systems, vol. 22, no. 1, pp. 25-42, 1998. [DOI:10.1016/S0198-9715(98)00017-9]
18. [18] J. J. Arsanjani, M. Helbich, and E. de Noronha Vaz, "Spatiotemporal simulation of urban growth patterns using agent-based modeling: The case of Tehran," Cities, vol. 32, pp. 33-42, 2013. [DOI:10.1016/j.cities.2013.01.005]
19. [19] J. Cheng and I. Masser, "Urban growth pattern modeling: a case study of Wuhan city, PR China," Landscape and urban planning, vol. 62, no. 4, pp. 199-217, 2003. [DOI:10.1016/S0169-2046(02)00150-0]
20. [20] X. Yang, X.-Q. Zheng, and L.-N. Lv, "A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata," Ecological Modelling, vol. 233, pp. 11-19, 2012. [DOI:10.1016/j.ecolmodel.2012.03.011]
21. [21] M. M. Aburas, Y. M. Ho, M. F. Ramli, and Z. H. Ash'aari, "Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an analytical hierarchy process and frequency ratio," International Journal of Applied Earth Observation and Geoinformation, vol. 59, pp. 65-78, 2017. [DOI:10.1016/j.jag.2017.03.006]
22. [22] M. Jafari, H. Majedi, S. M. Monavari, A. A. Alesheikh, and M. K. Zarkesh, "Dynamic simulation of urban expansion through a CA-Markov model Case study: Hyrcanian region, Gilan, Iran," European Journal of Remote Sensing, vol. 49, no. 1, pp. 513-529, 2016. [DOI:10.5721/EuJRS20164927]
23. [23] W. Gong, L. Yuan, W. Fan, and P. Stott, "Analysis and simulation of land use spatial pattern in Harbin prefecture based on trajectories and cellular automata-Markov modelling," International Journal of Applied Earth Observation and Geoinformation, vol. 34, pp. 207-216, 2015. [DOI:10.1016/j.jag.2014.07.005]
24. [24] K. Rajitha, C. Mukherjee, R. Vinu Chandran, and M. Prakash Mohan, "Land-cover change dynamics and coastal aquaculture development: a case study in the East Godavari delta, Andhra Pradesh, India using multi-temporal satellite data," International Journal of Remote Sensing, vol. 31, no. 16, pp. 4423-4442, 2010. [DOI:10.1080/01431160903277456]
25. [25] R. G. Pontius, "Quantification error versus location error in comparison of categorical maps," Photogrammetric engineering and remote sensing, vol. 66, no. 8, pp. 1011-1016, 2000.
26. [26] T. Munshi, M. Zuidgeest, M. Brussel, and M. van Maarseveen, "Logistic regression and cellular automata-based modelling of retail, commercial and residential development in the city of Ahmedabad, India," Cities, vol. 39, pp. 68-86, 2014. [DOI:10.1016/j.cities.2014.02.007]
27. [27] S. Berberoğlu, A. Akın, and K. C. Clarke, "Cellular automata modeling approaches to forecast urban growth for adana, Turkey: A comparative approach," Landscape and Urban Planning, vol. 153, pp. 11-27, 2016. [DOI:10.1016/j.landurbplan.2016.04.017]
28. [28] F. Fan, Y. Wang, and Z. Wang, "Temporal and spatial change detecting (1998-2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images," Environmental Monitoring and Assessment, vol. 137, no. 1, pp. 127-147, 2008. [DOI:10.1007/s10661-007-9734-y]
29. [29] M. Batty, Y. Xie, and Z. Sun, "Modeling urban dynamics through GIS-based cellular automata," Computers, environment and urban systems, vol. 23, no. 3, pp. 205-233, 1999. [DOI:10.1016/S0198-9715(99)00015-0]
30. [30] X. Fu, X. Wang, and Y. J. Yang, "Deriving suitability factors for CA-Markov land use simulation model based on local historical data," Journal of environmental management, vol. 206, pp. 10-19, 2018. [DOI:10.1016/j.jenvman.2017.10.012]
31. [31] Q. Guan, L. Wang, and K. C. Clarke, "An artificial-neural-network-based, constrained CA model for simulating urban growth," Cartography and Geographic Information Science, vol. 32, no. 4, pp. 369-380, 2005. [DOI:10.1559/152304005775194746]
32. [32] Y. Lee and H. Chang, "The simulation of land use change by using CA-Markov model: A case study of Tainan City, Taiwan," in Geoinformatics, 2011 19th International Conference on, 2011, pp. 1-4: IEEE. [DOI:10.1109/GeoInformatics.2011.5980819]
33. [33] S. Shekhar, "Suitable land assessment for urban expansion around Shimla, Himachal Pradesh (India)-MCE approach," Journal of Geomatics, vol. 11, no. 1, 2017.
34. [34] C. Kara and N. Akçit, "Using GIS for Developing Sustainable Urban Growth Case Kyrenia Region," ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 263-268, 2018. [DOI:10.5194/isprs-archives-XLII-3-W4-263-2018]
35. [35] M. Jelokhani-Niaraki and J. Malczewski, "A group multicriteria spatial decision support system for parking site selection problem: A case study," Land Use Policy, vol. 42, pp. 492-508, 2015. [DOI:10.1016/j.landusepol.2014.09.003]
36. [36] J. Malczewski and C. Rinner, Multicriteria decision analysis in geographic information science. Springer, 2015. [DOI:10.1007/978-3-540-74757-4]
37. [37] B. Feizizadeh, M. S. Roodposhti, P. Jankowski, and T. Blaschke, "A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping," Computers & geosciences, vol. 73, pp. 208-221, 2014. [DOI:10.1016/j.cageo.2014.08.001]
38. [38] J. Eastman, "Multi-criteria evaluation and GIS," Geographical information systems, vol. 1, no. 1, pp. 493-502, 1999.
39. [39] H. Bharath, M. Chandan, S. Vinay, and T. Ramachandra, "Modelling urban dynamics in rapidly urbanising Indian cities," The Egyptian Journal of Remote Sensing and Space Science, 2017. [DOI:10.1016/j.ejrs.2017.08.002]
40. [40] W.-M. Wey and K.-Y. Wu, "Using ANP priorities with goal programming in resource allocation in transportation," Mathematical and computer modelling, vol. 46, no. 7-8, pp. 985-1000, 2007. [DOI:10.1016/j.mcm.2007.03.017]
41. [41] S. J. Carver, "Integrating multi-criteria evaluation with geographical information systems," International Journal of Geographical Information System, vol. 5, no. 3, pp. 321-339, 1991. [DOI:10.1080/02693799108927858]
42. [42] W.-D. Wang, J. Guo, L.-G. Fang, and X.-S. Chang, "A subjective and objective integrated weighting method for landslides susceptibility mapping based on GIS," Environmental Earth Sciences, vol. 65, no. 6, pp. 1705-1714, 2012. [DOI:10.1007/s12665-011-1148-z]
43. [43] T.-C. Wang and H.-D. Lee, "Developing a fuzzy TOPSIS approach based on subjective weights and objective weights," Expert systems with applications, vol. 36, no. 5, pp. 8980-8985, 2009. [DOI:10.1016/j.eswa.2008.11.035]
44. [44] M. Alemi-Ardakani, A. S. Milani, S. Yannacopoulos, and G. Shokouhi, "On the effect of subjective, objective and combinative weighting in multiple criteria decision making: A case study on impact optimization of composites," Expert Systems with Applications, vol. 46, pp. 426-438, 2016. [DOI:10.1016/j.eswa.2015.11.003]
45. [45] S. E. Bodily, Modern decision making: a guide to modelling with decision support systems. McGraw-Hill, 1985.
46. [46] M. Kiavarz and M. Jelokhani-Niaraki, "Geothermal prospectivity mapping using GIS-based Ordered Weighted Averaging approach: A case study in Japan's Akita and Iwate provinces," Geothermics, vol. 70, pp. 295-304, 2017. [DOI:10.1016/j.geothermics.2017.06.015]
47. [47] J. Malczewski, "Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis," International journal of applied earth observation and geoinformation, vol. 8, no. 4, pp. 270-277, 2006. [DOI:10.1016/j.jag.2006.01.003]
48. [48] M. Kiavarz and M. Jelokhani-Niaraki, "Geothermal prospectivity mapping using GIS-based Ordered Weighted Averaging approach: A case study in Japan's Akita and Iwate provinces," Geothermics, vol. 70, pp. 295-304, 2017. [DOI:10.1016/j.geothermics.2017.06.015]
49. [49] T. Cooley et al., "FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation," in Geoscience and Remote Sensing Symposium, 2002. IGARSS'02. 2002 IEEE International, 2002, vol. 3, pp. 1414-1418: IEEE.
50. [50] J. R. Otukei and T. Blaschke, "Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms," International Journal of Applied Earth Observation and Geoinformation, vol. 12, pp. S27-S31, 2010. [DOI:10.1016/j.jag.2009.11.002]
51. [51] M. Rashmi and N. Lele, "Spatial modeling and validation of forest cover change in Kanakapura region using GEOMOD," Journal of the Indian Society of Remote Sensing, vol. 38, no. 1, pp. 45-54, 2010. [DOI:10.1007/s12524-010-0011-0]
52. [52] Y. Liu and S. R. Phinn, "Modelling urban development with cellular automata incorporating fuzzy-set approaches," Computers, Environment and Urban Systems, vol. 27, no. 6, pp. 637-658, 2003. [DOI:10.1016/S0198-9715(02)00069-8]
53. [53] J. Jiao and L. Boerboom, "Transition rule elicitation methods for urban cellular automata models," Innovations in Design & Decision Support Systems in Architecture and Urban Planning, pp. 53-68, 2006. [DOI:10.1007/978-1-4020-5060-2_4]
54. [54] T. L. Satty, "The analytical hierarchy process: planning, priority setting, resource allocation," RWS publication, Pittsburg, 1980.
55. [55] G.-L. Li and Q. Fu, "Grey relational analysis model based on weighted entropy and its application," in Wireless Communications, Networking and Mobile Computing, 2007. WiCom 2007. International Conference on, 2007, pp. 5500-5503: IEEE.
56. [56] C. Shannon and W. Weaver, "The math theory of communica," theUniversity of Illinois Press, Urbana, 1947.
57. [57] J. Eastman, "Idrisi for Windows, Version 2.0: Tutorial Exercises, Graduate School of Geography-Clark University, Worcester, MA," Google Scholar, 1997.
58. [58] R. R. Yager, "On ordered weighted averaging aggregation operators in multicriteria decisionmaking," IEEE Transactions on systems, Man, and Cybernetics, vol. 18, no. 1, pp. 183-190, 1988. [DOI:10.1109/21.87068]
59. [59] H. Jiang and J. R. Eastman, "Application of fuzzy measures in multi-criteria evaluation in GIS," International Journal of Geographical Information Science, vol. 14, no. 2, pp. 173-184, 2000. [DOI:10.1080/136588100240903]
60. [60] J. Malczewski, "GIS-based land-use suitability analysis: a critical overview," Progress in planning, vol. 62, no. 1, pp. 3-65, 2004. [DOI:10.1016/j.progress.2003.09.002]
61. [61] J. Malczewski and C. Rinner, "Exploring multicriteria decision strategies in GIS with linguistic quantifiers: A case study of residential quality evaluation," Journal of Geographical Systems, vol. 7, no. 2, pp. 249-268, 2005. [DOI:10.1007/s10109-005-0159-2]
62. [62] P. Couto, "Assessing the accuracy of spatial simulation models," Ecological Modelling, vol. 167, no. 1-2, pp. 181-198, 2003. [DOI:10.1016/S0304-3800(03)00176-5]
63. [63] M. K. Firozjaei, M. Kiavarz, S. K. Alavipanah, T. Lakes, and S. Qureshi, "Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-Markov chain modelling: A case of Babol city, Iran," Ecological Indicators, vol. 91, pp. 155-170, 2018. [DOI:10.1016/j.ecolind.2018.03.052]
64. [64] S. Panah, M. K. Mogaddam, and M. K. Firozjaei, "MONITORING SPATIOTEMPORAL CHANGES OF HEAT ISLAND IN BABOL CITY DUE TO LAND USE CHANGES," International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, vol. 42, 2017.
65. [65] I. A. Chandio, A. N. B. Matori, K. B. WanYusof, M. A. H. Talpur, A.-L. Balogun, and D. U. Lawal, "GIS-based analytic hierarchy process as a multicriteria decision analysis instrument: a review," Arabian Journal of Geosciences, vol. 6, no. 8, pp. 3059-3066, 2013. [DOI:10.1007/s12517-012-0568-8]
66. [66] R. B. Thapa and Y. Murayama, "Drivers of urban growth in the Kathmandu valley, Nepal: Examining the efficacy of the analytic hierarchy process," Applied Geography, vol. 30, no. 1, pp. 70-83, 2010. [DOI:10.1016/j.apgeog.2009.10.002]
67. [67] S. Ahmad and R. M. Tahar, "Selection of renewable energy sources for sustainable development of electricity generation system using analytic hierarchy process: A case of Malaysia," Renewable energy, vol. 63, pp. 458-466, 2014. [DOI:10.1016/j.renene.2013.10.001]
68. [68] O. S. Vaidya and S. Kumar, "Analytic hierarchy process: An overview of applications," European Journal of operational research, vol. 169, no. 1, pp. 1-29, 2006. [DOI:10.1016/j.ejor.2004.04.028]
69. [69] W. Chen and X. Hao, "An optimal combination weights method considering both subjective and objective weight information in power quality evaluation," in Advanced Electrical and Electronics Engineering: Springer, 2011, pp. 97-105. [DOI:10.1007/978-3-642-19712-3_12]
70. [70] M. Sahoo, S. Sahoo, A. Dhar, and B. Pradhan, "Effectiveness evaluation of objective and subjective weighting methods for aquifer vulnerability assessment in urban context," Journal of Hydrology, vol. 541, pp. 1303-1315, 2016. [DOI:10.1016/j.jhydrol.2016.08.035]
71. [71] W. Plata-Rocha, M. Gómez-Delgado, and J. Bosque-Sendra, "Simulating urban growth scenarios using GIS and multicriteria analysis techniques: a case study of the Madrid region, Spain," Environment and Planning B: Planning and Design, vol. 38, no. 6, pp. 1012-1031, 2011. [DOI:10.1068/b37061]
72. [72] Q. Zhang, Y. Ban, J. Liu, and Y. Hu, "Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China," Computers, Environment and Urban Systems, vol. 35, no. 2, pp. 126-139, 2011. [DOI:10.1016/j.compenvurbsys.2010.12.002]
73. [73] W. R. Tobler, "Geographical filters and their inverses," Geographical Analysis, vol. 1, no. 3, pp. 234-253, 1969. [DOI:10.1111/j.1538-4632.1969.tb00621.x]
74. [74] J. Malczewski and X. Liu, "Local ordered weighted averaging in GIS-based multicriteria analysis," Annals of GIS, vol. 20, no. 2, pp. 117-129, 2014. [DOI:10.1080/19475683.2014.904439]
75. [75] R. G. Pontius Jr, D. Huffaker, and K. Denman, "Useful techniques of validation for spatially explicit land-change models," Ecological Modelling, vol. 179, no. 4, pp. 445-461, 2004. [DOI:10.1016/j.ecolmodel.2004.05.010]
76. [76] Y. Feng and X. Tong, "Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change," Environmental monitoring and assessment, vol. 189, no. 10, p. 515, 2017. [DOI:10.1007/s10661-017-6224-8]
ارسال پیام به نویسنده مسئول


XML   English Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Karimi Firozjaei M, Sedighi A, Jelokhani-Niaraki M. Developing a model for simulating urban expansion based on the concept of decision risk: A case study in Babol city. jgit. 2019; 7 (3) :115-135
URL: http://jgit.kntu.ac.ir/article-1-745-fa.html

کریمی فیروزجائی محمد، صدیقی امیر، جلوخانی نیارکی محمدرضا. توسعه مدل پیش بینی گسترش فیزیکی شهر بابل مبتنی بر مفهوم درجه ریسک تصمیم‌گیری مکانی چندمعیاره. مهندسی فناوری اطلاعات مکانی. 1398; 7 (3) :115-135

URL: http://jgit.kntu.ac.ir/article-1-745-fa.html



دوره 7، شماره 3 - ( 9-1398 ) برگشت به فهرست نسخه ها
نشریه علمی-پژوهشی مهندسی فناوری اطلاعات مکانی Engineering Journal of Geospatial Information Technology
Persian site map - English site map - Created in 0.06 seconds with 31 queries by YEKTAWEB 4072