Investigation of the Expansion Trend and Related Effective Parameters Using Artificial Neural Network (Case Study: Tabriz)

Document Type : Research Article (Applied - Development)

Authors

1 Assistant Professor, Department of Geography and Remote Sensing, Kharazmi University, Tehran, Iran.

2 MSc Student of Geography and Sacred Defense, Urmia University, Urmia, Iran.

3 Assistant Professor, Department of Geography and Urban Planning, Kharazmi University, Tehran, Iran.

Abstract

Prediction of urban growth and the encroachment of urban construction are very important.  The planning of city managers for future management of the city and even planning to invest citizens are such cases that can be referred to. In this research, the trend of changes in urban development has been done in Tabriz, using artificial neural networks (ANN). To implement this, multi-temporal Landsat TM images for years 1990, 2000 and 2010 were used. To classify the mentioned image, MLC algorithm was used and post-classification comparison method was implemented for the change detection process. Feed-forward network architecture along with Back propagation algorithm has been used to correct the network weight during the process. Input variables include distance from the main roads, established areas, slope, service areas and aspect layers. The role of geological and tectonically parameters was also considered. Accuracy assessment of the simulation was performed for year 2010. In this process, the output of the neural network as forecasted image was compared to classified image of the same year. It shows the accuracy of 96%. The results show an increasing urban area growth via destroying vegetation as well as agricultural areas, in such rate that, it is forecasted more than 16000 hectares of nonurban areas will be transformed to urban areas during years 2010-2030. Also the results of this research indicate that the expansion direction of the city for the mentioned period will be toward the southeast.

Keywords

Main Subjects

References

 امیری، رضا. علیمحمدی، عباس و سیدکاظم علوی پناه. (۱۳۸۶). مطالعۀ تغییرپذیری فضایی ـ زمانی در ارتباط با کاربری/ پوشش زمین در منطقه شهری تبریز با استفاده از داده های حرارتی و انعکاسی TM و ETM+ لندست. محیط شناسی، شمارۀ ۴۳.
علوی پناه، سید کاظم. (۱۳۹۲). کاربرد سنجش از دور در علوم زمین (علوم خاک). تهران: انتشارات دانشگاه تهران، چاپ چهارم.
 فاطمی، سید باقر و رضایی، یوسف. (۱۳۸۹). مبانی سنجش از دور. انتشارات آزاده، چاپ دوم.
رضازاده، راضیه و میراحمدی، مهرداد (۱۳۸۸). مدل اتوماسیون سلولی روش نوینی در شبیه سازی رشد شهری، نشریه فناوری آموزش، جلد ۴، شمارۀ ۶.
زارع، مهدی. (۱۳۸۰). خطر زمین لرزه و ساخت و ساز در حریم گسل شمال تبریز و حریم گسلش گسل های زمین لرزه ای ایران. پژوهشنامه زلزله شناسی و مهندسی زلزله، سال ۴، شمارۀ ۲و ۳.
کامیاب، حمیدرضا و همکاران. (۱۳۹۰). کاربرد شبکۀ عصبی مصنوعی در مدل سازی توسعه شهری (مطالعۀ موردی: شهر گرگان). پژوهش های جغرافیایی انسانی، شمارۀ ۷۶. صص ۹۹-۱۱۳.
محمودی، سلمان و همکاران. (۱۳۹۴). بررسی میزان برابری، نابرابری و برخورداری مناطق شهرداری تبریز با استفاده از روش تاپسیس. اولین همایش علوم زمین و توسعه شهری، تبریز.
 Alkheder,S.&Shan;J., (2005). Urban growth simulation using remote sensing imagery and neural networks. In Third international symposium remote sensing and data fusion over urban areas (URBAN 2005) and the ۵th international symposium on remote sensing of urban areas (URS 2005), pp:14-16.
Araya, Y.H., (2009). Urban land use change analysis and modeling: a case sudy Setubal and Sesimbra Portugal Thesis , Institute for geoinformatics University of Munster
 Asghari Zamani, A., (2007). Evaluation and prediction of spatial expansion in cites (Case study: zanjan city). Ph.d Department of Geography and urban planning in Tabriz university.
 Atkinson, P.M., Tatnall, A.R.L., (1997). Introduction neural networks in remote sensing. International Journal of remote sensing, 18(4), pp: 699-709
Bella, K.P., Irwin, E.G., (2002). Spatially Explicit Micro-level Modeling of Land Use Change the Rural–urban Interface, Agricultural Economics, No,27, pp: 217-232.
 Carpenter, G.A., Gjaja, M.N., Gopal, S., Woodcock, C.E., (1997). ART neural networks for remote sensing: vegetation classification from Landsat TM and terrain data. Geoscience and Remote Sensing, IEEE Transactions on, 35(2), pp: 308-325.
 Chander, G., Markham, B.L., Helder, D.L., (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing environment, 113(5), pp: 893-903.
 Jensen, J.R., (2005). Introductory digital image processing: a remote sensing perspective.
Karul, C., Soyupak, S., (2003). A comparison between neural network based and multiple regression models for chlorophyll-a estimation. In Ecological Informatics (pp. 249-263). Springer Berlin Heidelberg
Pauchard, A., Aguayo, M., Peña, E., Urrutia. R., (2006). Multiple Effects of Urbanization on the Biodiversity of Developing Countries: The Case of a Fast-growing Metropolitan Area (Concepción, Chile), Biological Conservation, No,127, pp: 272-281.
Rumelhart, D.E., Hinton, G., Williams, R., (1986). Learning internal representations by error propagation. In D.E. Rumelhart & J.L. McClelland (Eds.), Parallel Distributed Processing Cambridge, MA: MIT Press, pp: 318-362.
 Rezazade, R., Mirahmadi, M., (2009). Cellular automata, a new approach in urban growth simulation. Journal of education technology, 4 (4).
Tayyebi, A., Pijanowski, B.C., Tayyebi, A.H. (2011). An urban growth boundary model using neural networks, GIS and radial parameterization: An application to Tehran, Iran. Landscape and Urban Planning,100(1), pp: 35-44.
Tayyebi, A. Christopher Pijanowski, B. Pekin, B., (2011). Two rule-based Urban Growth Boundary Models applied to the Tehran Metropolitan Area, Iran. Applied Geography, No,31, pp: 908-918.
 Triantakonstantis, D., Stathakis, D., (2015). Urban Growth Prediction in Athens, Greece, Using Artificial Neural Networks. Int. J. Civil Struct. Constr. Archit. Eng, 9, pp:193-197
 Werbos, P., (1974). Beyond regression: New tools for prediction and analysis in the behavioral Sciences. PhD Thesis, Cambridge, MA: Harvard University.
 Zarei, R., Alsheikh, A.A., (2012). Modeling of Urban development using cellular automata and genetic algorithm, case study: Shiraz City. Journal of Research and Planning, 3(11).
Journal of Urban Social Geography
Volume 2, Issue 3 - Serial Number 4
November 2016
Pages 47-64

Files

History

  • Receive Date: 02 August 2015
  • Revise Date: 05 October 2015
  • Accept Date: 04 November 2015

Share

How to cite

Statistics