مدل عصبی- فازی خطای افت در عملیات قطع هدایت‌شده با استفاده از روش خوشه‌بندی کاهنده

نوع مقاله : علمی- پژوهشی

نویسندگان

1 استادیار، گروه جنگل‌داری، دانشکده منابع طبیعی، دانشگاه گیلان

2 دانشیار، گروه جنگل‌داری، دانشکده منابع طبیعی، دانشگاه گیلان

چکیده

پژوهش پیش‌رو با استفاده از روش خوشه‌بندی کاهنده در سیستم عصبی- فازی تطبیقی به ارایه مدلی برای برآورد میزان خطای جهت افت درخت در قطع هدایت‌شده می‌پردازد. بدین منظور 95 اصله درخت در پارسل 207 سری دو حوضه آبخیز ناو توسط اکیپ عملیات قطع و صرف‌نظر از مهارت اره موتورچی‌ها، قطع شدند. اختلاف جهت پیش‌بینی‌شده و جهت افت واقعی درختان به‌عنوان خطای افت اندازه‌گیری شد. با درنظر گرفتن 12 عامل به‌عنوان عامل‌های مؤثر در میزان خطای افت و با به‌کارگیری دو نوع الگوریتم یادگیری، دو نوع تابع استنتاج و پنج نوع تابع عضویت برای متغیرهای ورودی، مدل‌های مختلف عصبی- فازی با روش خوشه‌بندی کاهنده ساخته و ارزیابی شدند. نتایج نشان داد که تابع عضویت ذوزنقه‌ای در ترکیب با سیستم استنتاج سوگنو مرتبه یک و الگوریتم یادگیری پس انتشار خطا بهترین عملکرد را در میان کلیه ترکیبات مورد نظر داشته‌اند. تحلیل حساسیت مدل نشان داد که مهم‌ترین عامل‌ها به‌ترتیب شیب زمین، زاویه سطح بن‌بری و بن‌زنی در امتداد حاشیه برش، قطر و زاویه دهانه بن‌زنی بوده‌اند و بقیه عامل‌ها تأثیر کمتری داشته‌اند. نتایج برآورد مدل نشان داد که گمان گروه قطع در تعیین انتخاب جهت افت درخت در شرایط پرشیب‌تر به واقعیت نزدیک‌تر بود. به‌علاوه، افزایش قطر درخت و باز کردن بیش از حد دهانه بن‌زنی با افزایش خطای قطع همراه بود.

کلیدواژه‌ها

عنوان مقاله [English]

A neuro-fuzzy model of error in directional felling operation using the subtractive clustering method

نویسندگان [English]

  • Esmaeil Ghajar 1
  • Ramin Naghdi 2
  • Mehrdad Nikooy 1
1 Assistant Prof., Department of Forestry, Faculty of Natural Resources, University of Guilan
2 Associate Prof., Department of Forestry, Faculty of Natural Resources, University of Guilan
چکیده [English]

The study presents models of error estimation in trees’ directional felling according to several effective factors using the subtractive clustering in the Adaptive Neuro-Fuzzy Inference System. A total number of 95 trees in the compartment 207 of 2nd district of Nav watershed in Guilan province were felled by felling group and regardless to the group’s skill, using manual chainsaw. The difference between predicted and real falling direction of trees was measured as felling error. To generate models, twelve independent variables were assumed to be the effective factors, and the two types of learning algorithm (LA), two inference types (IT) and five types of membership function (MF) for input variables were applied through the subtractive clustering method in the ANFIS. Results indicated that the trapezoidal type of MF in combination with the first-order type of Sugeno IT and the back propagation LA had the best performance among all combinations of setting parameters. The sensitivity analysis of the optimal model showed that the model was very sensitive to the changes in terrain slope, the angles of backcut and undercut surfaces and DBH, respectively. Results also revealed that felling group properly predicted the fall direction and performed the directional felling in the steeper terrain. In addition, the increase of DBH and opening too much the undercut notch have accompanied with the increase of felling error.

کلیدواژه‌ها [English]

  • chainsaw
  • membership function
  • Sugeno
  • ANFIS
  • cutting
  • soft computing

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