برآورد رویش ده‌ساله راش (Fagus orientalis Lipsky) با استفاده از مدل شبکه‌های عصبی مصنوعی و رگرسیون خطی چندگانه در جنگل‌های رامسر

نوع مقاله: علمی - پژوهشی

نویسندگان

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

2 کارشناس ارشد، موسسه تحقیقات جنگلها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران

3 دانشجوی دکتری محیط زیست، دانشکده منابع طبیعی، دانشگاه تهران، ایران

چکیده

در این تحقیق که در جنگل‌های رامسر در استان مازندران انجام شد رویش جنگل به کمک شبکه­ عصبی مصنوعی برآورد و با رویش واقعی جنگل که به­طور مستقیم و از اندازه­گیری در 20 قطعه‌نمونه ثابت یک هکتاری که در سال­های 1381 و 1391 از آماربرداری صد­در­صد محاسبه شده بود، مقایسه شد. رویش حجمی سالانه راش به­ترتیب 52/4 و 35/4 سیلو در هکتار برای رویش به طریق مستقیم و رویش برآوردی به روش شبکه عصبی مصنوعی بود. سپس تحلیل رگرسیون، به روش گام‌به‌گام انجام و بهترین مدل­ها گزینش شد. پس از انتخاب بهترین مدل، بررسی تحلیل حساسیت ورودی­ها انجام شد. نتایج نشان داد شبکه عصبی با دقت مناسبی می­تواند رویش و مقدار برش سالیانه را برآورد کند. مقدار R2، RMSE و MAE به­ترتیب 75/0، 17 و 60/13 در شبکه ­پرسپترون چندلایه نشان داد که شبکه عصبی MLP بیشترین دقت در برآورد را دارد. در تحلیل رگرسیون خطی چندگانه هم ضرایب تشخیص به­ترتیب 610/0 و 679/0 و خطای RMS مقادیر 5/1 و 42/1 برای مدل اول و دوم به­دست آمد. نتایج مربوط به تحلیل حساسیت ورودی­ها نشان داد که عوامل حجم، جهت، قطر برابرسینه و ارتفاع درخت بیشترین تأثیر را در مدل­سازی تعیین رویش دارند. مقایسه مدل­ها نشان داد استفاده از شبکه عصبی می­تواند مقدار رویش را با دقت مناسبی پیش‌بینی کند.

کلیدواژه‌ها


عنوان مقاله [English]

Ten-year estimation of Fagus orientalis Lipsky increment using artificial neural networks model and multiple linear regression Ramsar forests

نویسندگان [English]

  • Mahmoud Bayat 1
  • Majid Hassani 2
  • Sahar Heidari 3
1 Assistant Prof., Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
2 M.Sc. Graduate, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran
3 PhD. Student of Environment, Faculty of Natural Resources, University of Tehran, Iran
چکیده [English]

In this research, which was done in Ramsar forests in Mazandaran province, the forest increment was estimated using artificial neural network and compared with the actual increment of the forest which was directly measured in 20 sample plots of 1 hectare in 2002 and 2012. The annual volume growth of beech was 4.52 and 4.35 m3 in hectare for direct inventory and estimation by artificial neural networks. Then regression analysis was performed to compare the results, stepwise, and the best models were selected. After selecting the best model for forecasting growth, the sensitivity analysis of inputs was investigated.The results showed that the neural network with relatively good accuracy can estimate the annual growth and cutting. The value of R2, RMSE and MAE were 0.75, 17 and 13.6 (for 20 sample plot of one hectare) respectively in the multi-layer perceptron network. Results also showed that the MLP neural network had the highest accuracy in predicting and estimation. In the analysis of multiple linear regression, the coefficients of detection were 0.610 and 0.679, respectively, and RMSE was 1.5 and 1.42, respectively, for the first and second models respectively. The volume at the beginning of the period and the diameter at breast height had the greatest impact on the amount of wood produced. Comparison of the models showed the use of neural network can predict the growth rate with proper accuracy.

کلیدواژه‌ها [English]

  • Multi Layer Perceptron
  • Sensitive analysis
  • Regression
  • Volume increment
  • modeling
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