شبیه‌سازی تغییرات کاربری زمین شهر همدان در سال 2040 میلادی با استفاده از روش اتوماسیون سلولی و داده‌کاوی ترکیبی «رگرسیون بردار پشتیبان و سلول‌های خودکار فازی»

نوع مقاله : علمی - پژوهشی

نویسندگان

• سعید حاجی بابایی ¹
• کیانوش ذاکرحقیقی ²

¹ دانشگاه آزاد اسلامی واحد قزوین

² دانشگاه آزاد اسلامی واحد همدان

https://doi.org/10.22067/gusd.v4i1.55638

چکیده

توسعۀ کالبدی و رشد جمعیت شهرهای ایران در چند دهۀ اخیر دگرگونی‌هایی را پذیرفتند که سبب بروز عدم تعادل و ناهماهنگی در گسترش کالبدی شهرها و تغییرات پیش‌بینی‌نشده در توسعۀ فضایی شهرها شده است. در این پژوهش از تلفیق دو مدل اتوماسیون سلولی برای شبیه‌سازی تغییر کاربری در شهر همدان در سال 2040 میلادی و مدل رگرسیون بردار پشتیبان برای پایه‌ریزی قوانین انتقال غیرخطی برای شبیه‌سازی‌ اتوماسیون سلولی و از منطق فازی برای تعریف قوانین انتقال خطی و غیرخطی استفاده شده است. به منظور آزمون مدل شبیه‌سازی، تغییرات کاربری شهر همدان بررسی و پس از استخراج اطلاعات از پایگاه اطلاعات جغرافیایی و تصاویر ماهواره‌ای،‌ وضعیت‌ سلول‌ها در دوره‌های زمانی مختلف مورد ارزیابی قرار گرفت. نتایج حاصل از آزمون مدل نشان می‌دهد مدل تلفیقی مورد نظر قادر است با رفع پیچیدگی‌های اطلاعاتی و ابهامات ناشی از تحولات کالبدی شهر، مدلی مناسب برای تحلیل تحولات توسعه‌ای در گذشته و پیش‌بینی‌ جهات و میزان تغییرات کاربری فراهم آورد. در شهر همدان مساحت اراضی ساخته‌شده در شهر تا سال 2040 افزایش یافته و احتمالاً مساحت این پهنه به حدود 6350 هکتار برسد و رفته‌رفته مساحت اراضی باغستان نیز کمتر شود.

کلیدواژه‌ها

• اتوماسیون سلولی
• تغییر کاربری
• رگرسیون بردار پشتیبان
• منطق فازی
• شهر همدان

عنوان مقاله [English]

A Simulation of Land Use Change in Hamadan in 2040 Using Cellular Automation and Data Mining Method "Support Vector Vectorization and Fuzzy Automated Cells"

نویسندگان [English]

• Saeed Hajibabaei ¹
• Kianoosh Zakerhaghighi ²

¹ Qazvin Branch, Islamic Azad University

² Hamedan Branch, Islamic Azad University

چکیده [English]

Extended Abstract
1. Introduction
The development of the physical growth and population growth of cities has been on a steep rise over the past three decades. With new developments, cities were quickly adapted to changes, and the expansion of the service sector, and the concentration of industries and factories in cities have attracted people from many villages and small cities to major cities. Not only has these changes caused the disparity and dissonance of the physical expansion of the cities, but it also has greatly impacted the spatial development of cities. Cities may vary from a variety of perspectives, but contrary to these differences, cities have several features that make them similar. The dynamics and growth are two bases that exist in most cities. However, modeling of dynamism and growth, and models that involve the complexity of cities are very difficult. Therefore, these complexities make it harder to use old models for modeling because they are static, linear, cumulative, interconnected, and based on the theory of simple systems. So, in order to model urban systems, new methods should be employed that are dynamic, nonlinear, non-cumulative, discrete, and low-end. Therefore, this research aims to predict the future development of Hamadan city by employing a method that evaluates urban variables in a nonlinear way.
2. Methodology
Cities are among the most complex structures which are built through human societies. A cellular automaton is widely used for the simulation of complex nonlinear systems. In this article, we first explain the structural and the general principles of CA and then use fuzzy logic as a method of transforming rules of the CA model. These logic rules are desirable in dealing with complex non-linear relationships. An urban cellular automation is designed to model urban and regional systems that include simulation of urban projects such as urban development simulation, simulation of land use change, and so on. This is a proposed model for using simulation of user variations. The space-spatial variables used in the simulation of the geographic database are extracted and the variables of the change of use of satellite imagery will be obtained. Once the cells' position is determined in the next period, the cells are compared one by one. The process of performing this method consists of two main stages of classification and simulation. In the first step, linear categorizers are used to make decision making using vector regression. In the simulation stage, according to different variables, the probability of land use changes in the city will be predicted.
3. Results
The present study showed the extent, and quality of land use in the city by 2040, and then the future development of the city of Hamedan was simulated and predicted for the years to come. The simulation outcomes in the form of raster maps as well as the calculation of the city's physical development in 2040 using statistical methods revealed that if land use changes continued with this trend, the area of land built up by 2040 would increase and this area would probably approximately reach 6350 hectares, and the area of the lands of the garden and the tree will be reduced further. According to the calculations, the physical development of the city of Hamedan has been doubled over the past thirty years.
4. Conclusion
According to available statistics, the city of Hamadan has experienced a growing trend over the last 50 years. The city of Hamedan, due to its location, has many restrictions, however, the population of the city is increasing as a result of the urban area of Hamedan. The simulation of land use change in Hamadan city was used and the results indicate a good correlation between real and simulated models. Therefore, it can be concluded that the cellular automation model, based on the rules of transmission of regression vector support, is a useful tool for simulating urban systems. The strengths of this approach can be seen in terms of the physical outlook, the flexibility of the program, real-life prospects, the lack of involvement of non-programmers in the program preparation process, the attention to urban problems, and the lack of emphasis on the production of planning standards, etc. Given that most of the factors affecting the formation and dynamics of cities are dynamic and gradual, changes are affected by the areas under study and may even change from city to city. Therefore, it is suggested that different parts of the city be considered for optimal parameters of cellular automation and compared with each other. This modeling helps designers, planners, managers and other researchers to be able to predict the status of cities and other land use changes in the future.

کلیدواژه‌ها [English]

• Cellular automation
• User change
• Supporting vector regression
• Fuzzy logic
• Hamedan city

مراجع

1. حبیب، ف. و شکوهی، ع. (1391). شناخت و تحلیل مسائل شهری با استفاده از سیستم‌های فازی. هویت شهر، 6 (10)، 26-17

2. ذبیحی، ح. (1390). کنکاشی در نظریه‌ها، تئوری‌ها و اقدامات شهرسازی و معرفی برنامه‌ریزی فازی شهری. تهران: جهاد دانشگاهی (دانشگاه تربیت معلم)

3. رضازاده، ر. و میراحمدی، م. (1388). مدل اتوماسیون سلولی، روشی نوین در شبیه‌‌سازی توسعۀ کالبدی شهری، فن‌آوری آموزش، 4 (1)، 55-47

4. زارعی، ر. و آل‌شیخ، ع. (1391). مدل‌سازی توسعۀ شهری با استفاده از اتوماسیون سلولی و الگوریتم ژنتیک (منطقۀ مورد مطالعه: شهر شیراز). پژوهش و برنامه‌ریزی شهری. 3 (11)، 16-1

5. قهرمانی، م. (1391). توسعۀ گردش‌گری پایدار روستایی و جایگاه بافت‌های باارزش روستا (مطالعۀ موردی روستای سیمین شهر همدان). فضای گردش‌گری، 1 (2)، 80-65

6. کاسکو، ب. (1380). تفکر فازی. (علی غفاری، عادل مقصودپور ممتاز و جمشید قمیسی، مترجمان). تهران: انتشارات دانشگاه خواجه نصیرالدین طوسی

7. کامیاب، ح. (1390). کاربرد شبکۀ عصبی در مدل‌سازی توسعۀ شهری. پژوهش‌های جغرافیای انسانی، (76)، 113-99

8. ماجدی، ح. و پورجوهری، ا. (1392). ارزیابی تعریف حدود رشد شهر در طرح‌های توسعۀ شهری ایران (از طریق پایش 11 گونۀ شهری). هویت شهر، 7 (15)، 36-25

9. نوابخش، م.، نادری، ن. و مهدوی، م. ص، (1394). ارزیابی سهم عوامل توسعۀ کالبدی- فضایی و جمعیتی در بودجۀ مناطق شهری اصفهان. مطالعات جامعه‌شناختی شهری (مطالعات شهری)، 5 (15)، 26-1

10. Ahmadlou, A., Delavar, M. R., Tayyebi, A., & Shafizadeh Moghadam, H. (2015). Using multivariate adaptive regression spline and artificial neural network to simulate urbanization in Mumbai, India. Remote Sensing and Spatial Information Sciences, 40, 31-36

11. Ana, M., Nikolik, D., & Curfs, L. (2004). Probabilistic SVM outputs for pattern recognition using analytical geometry. Neurocomputing, 62, 293–303

12. Barredo, J. I., Kasanko, M., McCormick, N., & Lavalle, C. (2003). Modelling dynamic spatial processes: Simulation of urban future scenarios through cellular automata. Landscape and Urban Planning, 64, 145–160

13. Candau, J. T. (2002). Temporal calibration sensitivity of the SLEUTH urban growth model (Unpublished master’s thesis). University of California, Santa Barbara

14. Cheng, J. (2003). Modelling spatial and temporal urban growth (Unpublished doctoral dissertation). International institute for Geo-information Science and Earth Observation (ITC), The Netherlands

15. Chopard, B., Luthi, P. O., & Queloz, P. A. (1996). Cellular automata model of car traffic in a two-dimensional street network. Journal of Physics A: Mathematical and General, 29(10), 2325–2336

16. He, L., & Zhang, X. (2016). Fuzzy reliability analysis using cellular automata for network systems. Information Sciences, 348, 322–336

17. Junfeng, J. (2003). Transition rule elicitation for urban cellular automata models (Case study: Wuhan-China) (Unpublished master’s thesis). International Institute for Geo-information Science and Earth Observation (ITC), The Netherlands

18. Lou, J. (2006). Modeling urban growth and spatial structure in Nanjing, China with GIS and remote sensing. Milwaukee: The University of Wisconsin

19. Madevska-Bogdanova, A., Nikolik, D., & Curfs, L. (2004). Probabilistic SVM outputs for pattern recognition using analytical geometry. Neurocomputing, 62, 293–303

20. Nagel, K., & Schreckenburg, M. (1992). A cellular automaton model for freeway traffic. Journal de Physique, 2, 2221-2229

21. Pijanowski, B., Tayyebi, A., Doucette, J., Pekin, B., Braun, D., & Plourde, J. (2014). A big data urban growth simulation at a national scale: Configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environmental Modelling and Software, 51, 250-268

22. Smola, A., & Scholkopf, B., (2004). A tutorial on support vector regression. Statistics and Computing, 14, 199-222

23. Tayyebi, A., & DarrelJenerette, G. (2016). Increases in the climate change adaption effectiveness and availability of vegetation across a coastal to desert climate gradient in metropolitan Los Angeles, CA, USA. Science of the Total Environment, 548–549, 60–71

24. Tayyebi, A., Tayyebi, A. M., & Khanna, N. (2013). Assessing uncertainty dimensions in land-use change models: Using swap and multiplicative error models for injecting attribute and positional errors in spatial data. International Journal of Remote Sensing, 35(1), 149-170

25. Torrens, P. M., & O'Sullivan, D. (2002). Cellular automata and urban simulation: Where do we go from here? Environment and Planning B-Planning and Design, 28(2), 163-168

26. Vapnik, V. (1998). Statistical learning theory. New York, USA: Wiley

27. Wahle, J., Annen, O., Schuster, C., Neubert, L., & Schreckenburg, M. (2001). A dynamic route guidance system based on real traffic data. European Journal of Operational Research, 131(2), 302-308

28. White, R., & Engelen, G. (1997). Cellular automata as the basis of integrated dynamic regional modelling. Environment and Planning B-Planning and Design, 24(2), 235-246

29. White, R., & Engelen, G. (2000). High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers, Environment and Urban Systems, 24(5), 383–400

30. Wu, F. (1996). Changes in the structure of public housing provision in urban China. Urban Studies, 33(9), 1601-1627

31. Wu, F., & Webster, C. (1998). Simulation of natural land use zoning under free-market and incremental development control regimes. Computers, Environment and Urban Systems, 22(3), 241–256

32. Yang, Q., li, X., & Shi, X. (2008). Cellular automata for simulating land use changes based on support vector machines. Computers and Geosciences, 34(6), 592–602

33. Zhi, D. (1996). GIS-based spatial analysis, modeling and simulation a case study on the changing spatial structure of Hong Kong. Columbia: University of South Carolina