پیش‌بینی حجم صنعتی و هیزمی پهن‌برگان با استفاده از شبکه‌های عصبی مصنوعی (مطالعه موردی: سری گرازبن جنگل خیرودکنار، نوشهر)

نوع مقاله: علمی- پژوهشی

نویسندگان

1 کارشناسی ارشد جنگل‌داری، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران

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

3 استاد، گروه مهندسی ماشین های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، دانشگاه تهران، کرج، ایران

4 استادیار پژوهش، مؤسسه تحقیقات جنگلها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران

چکیده

تصمیم­‌گیری در منابع طبیعی اغلب به پیچید­گی­‌هایی فراتر از روش­‌های تجربی آماری منجر می­‌شود، بنابراین نیاز به راهکارهای نوین دارد. تکنیک شبکه­‌های عصبی مصنوعی با تقلید از مغز انسان و الگوبرداری از آن به  فرآیند حل مشکل می­‌پردازد. در این پژوهش به­ پیش­‌بینی حجم صنعتی و هیزمی درختان با استفاده از تکنیک هوش مصنوعی پرداخته شد. برای این منظور، 367 اصله از درختان نشانه­‌گذاری شده جنگل آموزشی- پژوهشی خیرودکنار نوشهر انتخاب و متغیرهای قطر برابر سینه، قطر کنده، ارتفاع کنده، ارتفاع کل، طول صنعتی، حداقل قطر میانه گرده‌­بینه، وضعیت درخت، گونه و عامل‌های توپوگرافی شامل شیب، جهت و ارتفاع از سطح دریا اندازه گیری شدند. کلیه متغیرها به‌عنوان ورودی شبکه درنظر گرفته شدند. برای مدل‌سازی از شبکه پرسپترون چندلایه استفاده شد. نتایج نشان داد که شبکه MLP با مقدار خطای جذر میانگین مربعات 0/233 و ضریب‌تبیین‌های 0/94 و 0/71 به‌ترتیب برای حجم­‌های صنعتی و هیزمی دارای دقت قابل قبولی برای پیش‌­بینی بود.

کلیدواژه‌ها


عنوان مقاله [English]

Prediction commercial and cordwood volume of broadleaves using Artificial Neural Networks (Case study: Gorazbon distric of Kheyrood forest, Nowshahr)

نویسندگان [English]

  • Fatemeh Gorzin 1
  • Manouchehr Namiranian 2
  • Mahmoud Omid 3
  • Mahmoud Bayat 4
1 M.Sc. Forestry, Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran
2 Prof., Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran
3 Prof., Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran
4 Assistant Prof., Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
چکیده [English]

Decision-making in natural resources often leads to complexities beyond the statistical empirical methods,therefore we need new solutions than algorithmic methods. Artificial neural networks (ANN) technology mimics the human brain in the process of problem solving.The aim ofthis studywas to predict the commercial volume and cordwood volume using this technique (Artificial Neural Network). For this purpose, 367 marked trees in the experimental and educational forest of Kheyrood were selected. Some factors including diameter at breast height, diameter at stump, stump height, total height, topographic factors (slope, aspect and elevation), species, tree situation and minimum median diameter of last log were measured. The factors were considered as input network. Multi-layer Perceptron network (MLP) was used for modeling. The result showed that Multi-layer Perceptron network (with the 0/94 and 0/71 R2, and 0/233 RMSE) has acceptable accuracy to predict the commercial and cordwood volume.

کلیدواژه‌ها [English]

  • Artificial Neural Networks
  • Kheyrood forest
  • Multi Layer Perceptron
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