چکیده:
شهر تبریز یکی از کلان شهرهای ایران است که گسترش و توسعه روزافزونی دارد. یکی از مشکلات موجود در مسیر توسعه شهرها، مدیریت نکردن صحیح آن و بی توجهی به عوامل موثر است. در سال های گذشته، شهر تبریز به دلیل مهاجرپذیر بودن از رشد فیزیکی بسیاری برخوردار بوده است. مدیریت صحیح رشد شهرها از جهات گوناگون از مسائل مهمی است که باید مدنظر قرار بگیرد. روش های متعددی برای تعیین مناطق مناسب رشد شهری وجود دارد. یکی از این روش ها در تعیین مناطق مناسب برای توسعه شهر روش شبکه عصبی است که در مطالعه حاضر نیز از آن استفاده شده است. در این مطالعه، برای تعیین مکان بهینه رشد شهری از سه گروه معیارهای اجتماعی-اقتصادی، کاربری زمین و بیوفیزیکی استفاده شد. برای مکان یابی مناطق مساعد رشد با روش شبکه عصبی، ۲۰۰ نقطه به عنوان نقاط آموزشی شبکه تهیه شدند و لایه های میانی نیز هفت عدد بود. نتایج مرتبط با اجزای شبکه نشان می دهد با دورشدن از امکانات و مناطق شهری، پتانسیل ها به شدت کاهش یافته است و بیشتر مناطقی که پتانسیل توسعه شهری دارند، در نزدیک ترین فاصله این امکانات و مناطق شهری قرار دارند. قسمت هایی از شهر که طی سال های گذشته به صورت پراکنده و نامنظم رشد داشته اند، با توجه به نتایج حاصل شده نامناسب هستند. همچنین حاشیه های نزدیک به هسته اصلی شهر که به خدمات شهری نیز دسترسی بیشتری دارند، برای رشد مناسب تر هستند، اما قسمت هایی که به صورت پراکنده در شمال غرب و جنوب شرق شهر توسعه یافته اند کاملا نامناسب هستند.
Tabriz, as one of the metropolises of Iran, is expanding every day. One of the problems that exists in the development of cities is the lack of proper management of it and failure to pay attention to effective factors.In recent years, the city of Tabriz has enjoyed a lot of physical growth due to its immigration status. Correct management of urban growth is one of the key issues that needs to be addressed. There are several methods for determining the appropriate areas for urban growth and one of the effective methods in determining the suitable areas for developing the city is the neural network method, which is used in this study. In this study, to determine the optimal location of urban growth, we use three groups of criteria, Socio-economic, land use and biophysical to locate growth areas with neural network approach 200 points were awarded as training points and 7 layers as intermediate layers were determined. Finally, the results showed with the network components by moving away from facilities and urban areas the potential has fallen sharply and most of the areas with urban development potential are within the nearest distance of these facilities and urban areas. Areas of the city that have grown periodically and regularly over the past years are inappropriate given the results. The results showed that the margins close to the core of the city, which have more access to urban services, are more suitable for growth and sprawling parts in the northwest of the city and south-east of the city are completely inappropriate.IntroductionThe important phenomena that have occurred in recent centuries in the social and economic life of different countries of the world are the emergence of numerous and new cities, the development of ancient cities, the advancement of urbanization and urban development. Urban development and changes in land use patterns lead to widespread social and environmental impacts. These include reducing natural spaces, increasing vehicle accumulation, reducing agricultural land with high production potential and reducing water quality. Urban development in any country is not coincidental and on the other hand, controlling its future requires careful planning. Understanding the right patterns of urban growth is needed to manage sustainable urban growth and plan for urban development. The high rates of urban population growth in Iran and the lack of urban infrastructure on the one hand, and the increasing trend of land use change, followed by the loss of valuable ecological land in urban and peri-urban areas due to marginalization, pollution industrial and other human activities, on the other hand, provide the necessity of modeling urban development.MethodologyThe data used in this research can be generally divided into two main categories: the data used to extract land use in the study area, which includes satellite imagery and data that are considered as effective factors on urban expansion and land use change. Identifying the variables that affect the creation of the main prerequisites for the development of land use models. In this study, independent groups of variables including socioeconomic, biophysical and land use were used. Since there are several decision making rules for exploiting these variables, in this study, the distance between these variables was considered as an indicator. To work with the artificial neural network firstly the effective parameters in urban development should be given as input to the network (INPOT), and then a number of educational points are provided to the network, so that the network uses these points (TARGET) to measure the impact of each It determines the input layers, in fact the network has learned the necessary training to deal with new areas. After determining the number of hidden layers in the network structure, the entire study area is provided to the trained network and the network is using what has learned the whole province to zoning with the potential of urban development.Result and discussionMLP network with 16 input layers (effective factors in urban development), 7 intermediate layers (test and error method), a neuron in the output layer that leads to an outline map (final map of urban development potential) and the Laufenberg- Marquette was executed And thus, the training was provided to meet new samples. The network stopped after 15 repetitions and got the necessary training. The network repeats 15 to the best possible state, the highest correlation and the lowest error.ConclusionIn this study, natural, social, economic factors and urban services such as hospitals, business centers and educational facilities are considered. The results of the research have shown the vicinity of the city for more suitable development. And previously scattered areas have been found to be inappropriate. Like the industrial areas of Atlas in the northwest of the city due to lack of access to urban services and placing in the fault domain was inappropriate for development or the Kandrood village in the southeast of Tabriz, which has been connected to the city over time, is not a good place for urban development because the centers do not have access to services, especially hospitals. And on the other hand, this part of Tabriz has garden features and the expansion of residential areas in this part will be accompanied by the destruction of gardens. It seems that the Zanjan-Tabriz highway on the southeast and the presence of gardens, it has led the development of the city over time but the results show if complete planning is done and to focus on all the influential cases, these areas will not be suitable for development. Areas that are appropriate for development in the final map, in the south, south east and north of the city are unused land and only in parts of the West they include some agricultural land that can ignore them to development of the city.
خلاصه ماشینی:
ارزيابي پتانسيل و الگوي رشد بهينۀ شهر تبريز مبتني بر استفاده از شبکه هاي عصبي رحيمه رستمي - دانشجوي دکتري سنجش ازدور و سيستم اطلاعات جغرافيايي، دانشکده جغرافيا، دانشگاه تهران ميلاد باقري - کارشناس ارشد سنجش ازدور و سيستم اطلاعات جغرافيايي، دانشکده جغرافيا، دانشگاه تهران ميثم ارگاني *- استاديار سنجش ازدور و سيستم اطلاعات جغرافيايي، دانشکده جغرافيا، دانشگاه تهران مصطفي حسن وند- کارشناس ارشد سيستم اطلاعات جغرافيايي و سنجش ازدور دانشگاه تبريز پذيرش مقاله : ١٣٩٧/١١/٢٩ تأييد نهايي: ١٣٩٨/٣/٢٩ چکيده شهر تبريز يکي از کلان شهرهاي ايران است که گسترش و توسعۀ روزافزوني دارد.
يکي از اين روش ها در تعيين مناطق مناسب براي توسعۀ شهر روش شبکۀ عصبي است که در مطالعۀ حاضر نيز از آن استفاده شده است .
براي مکان يابي مناطق مساعد رشد با روش شبکۀ عصبي ، ٢٠٠ نقطه به عنوان نقاط آموزشي شبکه تهيه شدند و لايه هاي مياني نيز هفت عدد بود.
براي کار با شبکۀ عصبي مصنوعي ابتدا بايد پارامترهاي مؤثر در توسعۀ شهري ، به عنوان لايه هاي ورودي به شبکه (INPUT) معرفي شود که اين عوامل از کاربري شهر، طبقه بندي سال ٢٠١٧ و DEM ١ منطقه توليد شدند.
, 2016, Application of Multilayer Perceptron Neural Network (Mlp) in Determination of Urban Solid Waste Landfill with Emphasis on Hydrogeomorphic Properties (Case Study: Fereydoun Shahr City), Journal of Ecology, Vol. 42, No. 2, PP.
, 2015, The Application of Artificial Neural Network and Fuzzy Logic Integration in Urban Development (Case Study: Marivan City), Master's Thesis, University of Tehran.