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مقایسه بین شبکه عصبی مصنوعی و تحلیل رگرسیون در برآورد مدت زمان قطع درخت | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 7، دوره 20، شماره 4 - شماره پیاپی 50، دی 1391، صفحه 607-595 اصل مقاله (389.99 K) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2012.107464 | ||
| نویسندگان | ||
| هادی بیاتی1؛ اکبر نجفی* 2؛ پرویز عبدالمالکی3 | ||
| 1دانشجوی کارشناسی ارشد، گروه جنگلداری، دانشکده منابع طبیعی و عوم دریایی دانشگاه تربیت مدرس | ||
| 2گروه جنگلداری، دانشکده منابع طبیعی و علوم دریایی دانشگاه تربیت مدرس | ||
| 3دانشیار، دانشکده علوم زیستی دانشگاه تربیت مدرس | ||
| چکیده | ||
| قطع درخت در بین مؤلفه‌های بهره‌برداری، اهمیت زیادی دارد. برآورد تولید تجهیزات جنگلی، بخش مهمی از مدیریت هزینه‌ها در یک واحد جنگلداری است که با کاهش هزینه‌های عملیات همراه است. به عبارت دیگر، هزینه‌های بالای سرمایه‌گذاری در بهره‌برداری جنگل، دلیل خوبی برای تحقیقات مهندسی جنگل و همچنین مدل‌سازی زمان می‌باشد. روشهای زیادی مانند انواع رگرسیون‌ها، منطق فازی، شبکههای عصبی و غیره برای پیش‌بینی زمان قطع وجود دارد که به کمک آنها می‌توان به ارتباط منطقی بین زمان قطع درخت و متغیرهای مستقل موجود دست یافت و برای عملیات آینده میزان زمان قطع درخت را پیش‌بینی نمود. در این تحقیق از تحلیل رگرسیون و شبکههای عصبی پرسپترون چند لایه و تابع شعاع مدار برای پیش‌بینی زمان قطع درخت در جنگلهای شرکت نکاچوب استفاده شد. به‌منظور جمع‌آوری داده‌های زمان قطع، از روش مطالعه زمانی پیوسته استفاده شد. بدین منظور تعداد 84 درخت از درختان نشانه‌گذاری شده انتخاب شد و زمان خالص قطع درخت با استفاده از شبکه پرسپترون چندلایه و تابع شعاع مدار، و همچنین روش رایج تحلیل رگرسیون پیش‌بینی گردید. نتایج نشان داد که شبکه عصبی تابع پایه شعاعی نسبت به شبکه عصبی پرسپترون چندلایه دارای دقت بیشتری در برآورد زمان قطع درخت می‌باشد. همچنین مقایسه معیارهای ارزیابی شبکه عصبی مصنوعی با رگرسیون گام‌به‌گام نشان داد که شبکه عصبی MLP و RBF به‌ترتیب دارای مقدار RMSE 0/94 و 81/0 بوده، در حالی که مقدار RMSE مدل رگرسیون 15/1 می‌باشد. | ||
| کلیدواژه‌ها | ||
| مهندسی جنگل؛ مطالعه زمانی؛ تابع پایه شعاعی؛ پرسپترون چندلایه؛ بهره‌برداری جنگل | ||
| عنوان مقاله [English] | ||
| Comparison between artificial neural network (ANN) and regression analysis in tree felling time estimation | ||
| نویسندگان [English] | ||
| Hadi Bayati1؛ Akbar Najafi2؛ Parviz Abdolmaleki3 | ||
| 1M.Sc. student of forestry, Faculty of Natural Resources and Marine Sciences, University of Tarbiat Modares | ||
| 2Assistant prof., Faculty of Natural Resources and Marine Sciences, University of Tarbiat Modares | ||
| 3Associated prof, Faculty of Biological Sciences, University of Tarbiat Modares | ||
| چکیده [English] | ||
| Tree felling is a most important one among the tree harvesting components. Production estimation of forest equipments is an important part of cost management in forestry operational units which is associated with reduction of the operating expenses. In other words, the high cost of investment in forest utilization, is a good reason for forest engineering research and modeling time. Many techniques such as regression, fuzzy logic, neural networks, etc. are utilized to estimate trees felling time. They make a logical connection between the tree felling time and the independent variables and could be used to predict the tree felling time for the future operations. In this study the regression analysis, two neural network models, multi-layer perceptron (MLP) and radial basis function (RBF) were used to predict the trees felling time in the cutting operations of the Neka Choob Co. In order to collect the felling time data, the time continuous study method was applied. For this purpose, 84 trees were selected from the marked stands and the net felling time was estimated, using the Multi Layer Perceptron and Radial Basis Function and also by the common method of linear regression analysis. The results showed that the Radial Basis Function network provided more accurate results in estimating the net tree felling time than the MLP neural network. Comparing the evaluation criteria of ANN with the stepwise regression methods, showed that MLP and RBF neural networks had RMSE value of 0.94 and 0.81, respectively whereas the RMSE value of the regression model was 1.15. | ||
| کلیدواژه‌ها [English] | ||
| Forest engineering, time study, Radial basis function, Multi Layer Perceptron, Forest harvesting | ||
| مراجع | ||
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