شبیه‌سازی زی‌توده تجاری توده‌های آمیخته راش در جنگل‌های هیرکانی

نوع مقاله : علمی- پژوهشی

نویسنده

دکتری جنگل‌داری، مؤسسه تحقیقات جنگلها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی

چکیده

تنه تجاری درختان جنگل‌های آمیخته راش علاوه‌بر حداکثر سهم ارزش نقدی و موجودی حجمی، در قالب زی‌توده تجاری به‌عنوان بزرگ‌ترین ذخایر کربن آلی در جنگل‌های شمال محسوب می‌شوند. هدف اصلی پژوهش پیش‌رو دست‌یابی به حداکثر قطعیت و دقت تخمین مقادیر زی‌توده مورد مطالعه بود. پس از قطع و استحصال 174 درخت در جنگل آمیخته راش گلندرود نور، هر بخش از تنه در عرصه وزن شد و قطعات چوبی نیز از هر بخش مذکور با ابعاد ثابت، تکه‌برداری شدند و برای اندازه‌گیری و محاسبه چگالی ویژه در شرایط دمای آون قرار گرفتند. شبیه‌سازی مقادیر زی‌توده مورد مطالعه با استفاده از تکنیک شبکه عصبی مصنوعی انجام شد. برای ارایه دقت روند شبیه‌سازی، الگوسازی آلومتریک با پارامترهای مختلف نیز تبیین شد. از لایه‌های ورودی قطر برابر سینه، ارتفاع تجاری تنه و چگالی ویژه با ترکیب مختلف در الگوسازی آلومتریک و شبیه‌سازی شبکه عصبی استفاده شد. معماری مختلف توپولوژی شبکه الگوریتم پس‌انتشار خطا با تعداد نورون‌های متفاوت شامل توابع انتقالی لجستیک سیگموئیدی و تانژانت سیگموئیدی در لایه‌های پنهان، دقت متفاوتی از برآورد متغیر پاسخ ارایه دادند. قطر به‌عنوان مهم‌ترین عامل تأثیرگذار در شبکه عصبی و معادلات آلومتریک محسوب شد و با افزایش ارتفاع و چگالی ویژه علاوه‌بر قطر، روند قطعیت برآورد افزایش یافت. نتایج نهایی برمبنای کلیه شاخص‌های اعتبارسنجی و ریشه میانگین مربعات خطای بین تخمین و مشاهدات نشان داد که اگرچه دقت برآوردی بین الگوسازی آلومتریک و شبیه‌سازی شبکه عصبی دارای اختلاف جزئی بود، اما خروجی بهینه به‌دست‌آمده از شبیه‌سازی با سه لایه ورودی قطر، ارتفاع و چگالی ویژه، یک لایه پنهان و 20 نورون عصبی حاوی تابع تانژانت سیگموئیدی دارای دقت بیشتری برای پیش‌گویی بود که قابلیت اجرا در سطح وسیعی از جنگل مورد مطالعه را دارد.

 

کلیدواژه‌ها

عنوان مقاله [English]

Simulating commercial biomass in the Hyrcanian mixed-beech stands

نویسنده [English]

  • Ali Asghar Vahedi
Ph.D. Forestry, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO)
چکیده [English]

The commercial bole of trees in the mixed-beech forests contributes the majority of biomass and of carbon pool, and is associated with the majority of monetary values in the Hyrcanian forests of Iran. This research aims to accurately predict commercial biomass compared to the allometric equations and field measurements in the third district of Glandroud forests in Noor. After harvesting of the trees, each part of the bole was weighed in the field and wood pieces were extracted from each part. The pieces were then oven-dried, on which the specific wood density was measured. Biomass was simulated by artificial neural network (ANN) including the FFBP network. Allometric equations (logarithmic multiple linear regressions and transformed power function models) with different parameters were examined to study the simulation uncertainty. Diameter at breast height, commercial height and specific wood density (WD) were inputs to the allometric functions and ANN simulation. Architectures of different topology of studied network including transfer functions of Log-sigmoid and Tan-sigmoid with variety of hidden layers and neuron members returned different error estimations of forest commercial biomass. Diameter was one of the most effective factors to predict biomass using ANN. Moreover, increasing height and WD in the ANN reduced the uncertainty of simulation outputs. Adding height and WD with the different combinations in the allometric models increased the accuracy of response variable prediction. The root mean squared errors (RMSE) showed that although there was slight differences in the estimation accuracies of ANN and allometric models, the optimal ANN outputs were of lower uncertainty to spatially predict the response
 

کلیدواژه‌ها [English]

  • Artificial intelligence
  • Carbon sequestration
  • commercial bole
  • regression analysis

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