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پتانسیل‌یابی مناطق توسعۀ شهری با استفاده از شبکۀ عصبی مصنوعی (مطالعۀ موردی: شهر کرمانشاه) | ||
| پژوهش‌های جغرافیای برنامه‌ریزی شهری | ||
| مقاله 10، دوره 6، شماره 1، فروردین 1397، صفحه 175-196 اصل مقاله (2.13 M) | ||
| نوع مقاله: پژوهشی - کاربردی | ||
| شناسه دیجیتال (DOI): 10.22059/jurbangeo.2018.246068.816 | ||
| نویسندگان | ||
| میلاد باقری1؛ محمدرضا جلوخانی نیارکی* 2؛ عبدالخالق چارکانه1؛ کیوان باقری3 | ||
| 1دانشجوی کارشناسی ارشد سنجش از دور و سیستمهای اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران | ||
| 2استادیار گروه سنجش از دور و سیستمهای اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران | ||
| 3دانشجوی دکتری سنجش از دور و سیستمهای اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران | ||
| چکیده | ||
| رشد سریع شهرنشینی و توسعة شهری به‌ویژه در کشورهای درحال‌توسعه، به درک الگو و فرایندهای پیچیدة رشد شهری با روش علمی و کارآمد نیاز دارد. لازمة ایجاد رشد شهری پایدار و برنامه‌ریزی توسعة شهری، درک الگوهای صحیح رشد شهری است. کرمانشاه نهمین شهر پرجمعیت کشور و یکی از چهار شهر نخست ایران از نظر حادبودن معضل حاشیه‌نشینی است. هدف این پژوهش، بررسی پتانسیل توسعة شهری در این شهر است. بدینمنظور، شبکة عصبی پرسپترون چندلایه (MLP) با الگوریتم آموزش لونبرگ- مارکوات به‌کار رفت و داده‌های مؤثر در توسعة شهری به‌عنوان لایه‌های ورودی به شبکه تعیین شد. این لایهها که در سه گروه اجتماعی-اقتصادی، کاربری زمین و بیوفیزیک قرار می‌گیرند، شامل 16 لایه هستند. در ادامه، پانصد نقطه به‌عنوان نقاط آموزشی شبکه تهیه و ۱۲ لایة میانی نیز تعیین شدند. مطابق نتایج، با دورشدن از امکانات و مناطق شهری، پتانسیل‌ها به‌شدت کاهش پیدا می‌کنند و بیشتر مناطق دارای پتانسیل توسعة شهری در نزدیکترین فاصلة این امکانات و مناطق شهری قرار دارند. بیشترین مناطق پتانسیل‌دار توسعة شهری، در جنوب‌غرب شهر کرمانشاه و در اطراف جاده‌های اصلی کرمانشاه-اسلام‌آباد و کرمانشاه-کنگاور واقع است. مناطق شمالی شهر به‌دلیل ارتفاع و شیب زیاد، پتانسیل اندکی برای توسعه دارند. همچنین ضریب رگرسیون کلی 9۵ درصدی شبکه که حاصل شرکت تمامی داده‌ها در شبکه است، کارایی زیاد شبکة عصبی پرسپترون چندلایه را در این مطالعه نشان می‌دهد. | ||
| کلیدواژه‌ها | ||
| پتانسیل یابی؛ توسعة شهری؛ شبکة عصبی؛ طبقه بندی؛ MLP | ||
| عنوان مقاله [English] | ||
| Evaluating the Potential of Urban Development Areas using Artificial Neural Network (Case Study: Kermanshah City) | ||
| نویسندگان [English] | ||
| Milad Bagheri1؛ Mohammadreza Jelokhani-Niaraki2؛ Abdolkalegh Charkaneh1؛ Keyvan Bagheri3 | ||
| 1Master Student in GIS and Remote Sensing, Faculty of Geography, University of Tehran, Tehran, Iran | ||
| 2Assistant Professor of GIS and Remote Sensing, Faculty of Geography, University of Tehran, Tehran, Iran | ||
| 3PhD Candidate in GIS and Remote Sensing, Faculty of Geography, University of Tehran, Tehran, Iran | ||
| چکیده [English] | ||
| Introduction Some of the important phenomena occurred in recent centuries in the social and economic life of different countries of the world are the emergence of numerous new cities, development of ancient cities, advances in urbanization and urban development. Urban development and changes in land use patterns lead to widespread social and environmental impacts including reduction in natural spaces, increases in vehicle accumulation, reduction in agricultural fertile lands with high production potential and degradation of water quality. Urban development in any country is not coincident with other aspects and, on the other hand, controlling future development requires careful planning. Understanding the right patterns of urban growth is needed to manage sustainable urban growth and planning for urban development. The high rates of urban population growth in Iran and the lack of urban infrastructure in one hand and the increasing trend of land use change, followed by the loss of valuable ecological landuses in urban and peri-urban areas andindustrial pollution provide the necessity of modeling for urban development. Methodology The data used in this research can be generally divided into two main categories: the data used to extract land uses in the study area, e.g., satellite imagery, and the data considered as effective factors on urban expansion and land use change. Identifying the variables affecting the creation of the main prerequisites for the development of land use models, we try to use independent groups of variables including socioeconomic, biophysical and land use in this study. 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, the effective parameters in urban development should be given as input to the network (INPUT), and then a number of educational points are provided to the network for using 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 train the network for zonnation of the potential areas of urban development. MLP network with 16 input layers (effective factors in urban development), 12 intermediate layers (test and error method), a neuron in the output layer as an outline map (final map of urban development potential), and the Leuvenberg Marquard training algorithm was executed and, thus, the network was trained with new samples. Results and discussion The network stopped after 44 repetitions and got the necessary training. In repetition 38, the optimal possible condition has the highest correlation and the least error. Given the 0.93 second factor, it can be ensured that network over-network learning is well prevented. Finally, the total regression coefficient of the network is resulted from the company total data network (95%). Then, the entire study area was evaluated by the network and the network based on the weight of the criteria received the training. The output from this stage was a valuable layer between zero and one. Conclusion It can be clearly seen that with the evolution of urban facilities and potentials, the vast majority of regions with urban development potential are located at the closest distance between these facilities and urban areas, especially the main roads of the city. The areas displayed in blue have the greatest potential for urban development. The most potential areas of urban development are located in the southwest of Kermanshah and around the main roads of Kermanshah-Islamabad and Kermanshah-Kangavar. The northern areas of the city have a low development potential due to their height and slope. The results of the study could be used to identify the areas for urban development to prevent development of irregular towns that have severe impacts on urban ecosystems and the lives of urban residents, as well as the loss of city capital. The future planning of the city of Kermanshah is relying on sustainable development with the least damage and inconsistencies in the agenda of urban managers and planners. | ||
| کلیدواژه‌ها [English] | ||
| urban development potential, neural network, Classification, MLP | ||
| مراجع | ||
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