پیش بینی مناطق دارای توان اکوتوریسم با شبکه عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی جنگل، دانشکده منابع طبیعی، دانشگاه تهران

2 استاد گروه جنگلداری و اقتصاد جنگل، دانشکده منابع طبیعی، دانشگاه تهران، ایران

3 دانشیار گروه جنگلداری و اقتصاد جنگل، دانشکده منابع طبیعی، دانشگاه تهران، ایران

4 استاد گروه مهندسی مکانیک ماشین‏های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، دانشگاه تهران، ایران

چکیده

استفاده تفرجی از منطقه باید مطابق توان محیط‏زیستی آن انجام گیرد. بنابراین، این پژوهش با هدف ارایه یک روش برای مدل‏سازی و رتبه‏بندی مناطق دارای توان اکوتوریسم انجام شد. بدین منظور از روش سیستمی مخدوم با توجه به ویژگی‏های منطقه و شبکه عصبی پرسپترون چندلایه (MLP) برای ارزیابی توان اکولوژیکی منطقه حفاظت ‏شده ارسباران استفاده شد. در گام نخست منابع اکولوژیکی و اقتصادی- اجتماعی شناسایی و نقشه‏های آن‏ها تهیه شدند، سپس با تجزیه و تحلیل و جمع‏بندی داده‏ها در نرم‏افزار ArcGIS نقشه توان اکوتوریسم حاصل شد. در مرحله بعد با استفاده از نتایج روش سیستمی، شبکه عصبی آموزش داده شد و ساختارهای مختلف آن مورد ارزیابی قرار گرفتند و در نهایت نقشه مناطق مناسب گردشگری براساس خروجی شبکه عصبی مدل‏سازی شد. در مرحله آخر با دخالت دادن معیارهای اقتصادی- اجتماعی و جاذبه‏های تفرجی اولویت‏بندی و ارزیابی نهایی انجام گرفت. ارزیابی توان اکولوژیکی با روش سیستمی نشان داد، منطقه دارای توان برای تفرج متمرکز طبقه دو (06/0%) و تفرج گسترده طبقه دو (33/10%) است. توپولوژی 3-9-7 به عنوان بهترین طبقه‏بندی با دقت کلی 98% جهت طبقه‏بندی مناطق تفرجی انتخاب شد و بهترین عملکرد شبکه عصبی به کلاس تفرج متمرکز و کمترین عملکرد به کلاس تفرج گسترده تعلق گرفت. براساس نقشه مدل‏سازی شده، 17/0% منطقه به تفرج متمرکز طبقه 2، 09/10% به تفرج گسترده طبقه 2 و 74/89% به نامناسب برای تفرج اختصاص یافت. مطالعه حاضر نشان داد شبکه عصبی مصنوعی قابلیت طبقه‏بندی مناطق مناسب گردشگری را با دقت بالا دارد.

کلیدواژه‌ها

عنوان مقاله [English]

Predicting of Areas with Ecotourism Capability Using Artificial Neural Network

نویسندگان [English]

  • Manijeh Talebi 1
  • Baris Majnounian 2
  • Majid Makhdoum 2
  • Ehsan Abdi 3
  • Mahmoud Omid 4
1 PhD student of Forest Engineering, Faculty of Natural Resources, University of Tehran
2 Profe. Department of Forestry and forest Economics, Faculty of Natural Resources,
3 Assoc. Profe. Department of Forestry and forest Economics, Faculty of Natural Resources, University of Tehran, Iran
4 Profe. Department of Mechanical Engineering Agricultural Machinery, Faculty of Agricultural Engineering and Technology, University of Tehran, Iran
چکیده [English]

Recreational use of the area must be performed conforms to its ecological capability. Therefore, this study was carried out with the aim of providing a method for modelling and ranking the areas with ecotourism capability. For this purpose, Makhdoum systemic method, regarding to the region specifications, and multi-layer perceptron artificial neural network (MLP) were used to evaluate the ecological capability of Arasbaran protected area. At first, ecological and socio-economic resources were identified and maps of them were provided. Then, ecotourism capability map was prepared by analyzing and overlaying of data in ArcGIS. In the next step, using the results of the systemic method, neural network was trained and its various structures were evaluated. Finally, map of the suitable tourism areas was modeled based on neural network output. In the end, using the socio-economic criteria and recreational attractions, prioritize and final evaluation was performed. Regarding to the systemic analysis, the area has the capability for intensive recreation class-2 (0.06%), and extensive recreation class-2 (10.33%). Topology 7-9-3 was selected as the best classifier with an overall accuracy of 98% for recreational regions classification. The best and the lowest of neural network application were shown to belong to intensive recreation class, and extensive recreation class, respectively. Based on modeled map, 0.17%, 10.09%, and 89.74% of the area were shown to belong to intensive recreation-class 2, extensive recreation-class 2, and unsuitable for recreation, respectively. This study showed artificial neural network has potential for classification of the suitable tourism areas with high accuracy.
 

کلیدواژه‌ها [English]

  • Ecological capability evaluation
  • Ecotourism
  • Systemic analysis
  • Artificial neural network
  • Arasbaran protected area

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پژوهش های محیط زیست
دوره 12، شماره 23
مجله پژوهشی
شهریور 1400
صفحه 55-66

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