مدل‌سازی آشفتگی انبوهی جنگل در ارزیابی محیطی با استفاده از شبکه عصبی مصنوعی

نوع مقاله: علمی- پژوهشی

نویسنده

استادیار، گروه محیط زیست طبیعی و تنوع زیستی، دانشکده محیط زیست، دانشگاه محیط زیست

چکیده

ارزیابی اثرات محیط زیستی به‌عنوان یک ابزار اساسی برای مدیریت محیط زیستی و توسعه پایدار شناخته شده است، اما زمانی‌که به مقادیر کمی برای تصمیم‌گیری نیاز است، ارزیابی اثرات دچار مشکل می‌شود و نیاز به مدل‌سازی آشکار است. هدف از پژوهش پیش‌رو طراحی و پیاده‌سازی یک سامانه مبنی بر شبکه عصبی مصنوعی با استفاده از اجزای اکوسیستم، فعالیت‌های طرح جنگلداری و میزان آشفتگی تراکم تاج‌پوشش اکوسیستم جنگلی (انبوهی جنگل) بود. پژوهش پیش‌رو در سه بخش پاتم، نم‌خانه و گرازبن جنگل خیرود نوشهر انجام شد. واحدهای همگن محیط زیستی با استفاده از منابع اکولوژیکی و ابزار دقیق GIS تهیه شد. با انتخاب الگوریتم مناسب در محیط شبکه‌های عصبی مصنـوعی در نرم‌افزار NeuroSolutions 5، انبوهی جنگل براساس مقادیر کمی و کیفی شرایط اکولوژیک و فعالیت‌های انسانی شبیه‌سازی شد. شبکه پرسپترون چندلایه با یک لایه پنهان و چهار نرون در هر لایه با توجه به بیشترین مقدار ضریب تعیین (برابر با 0/9864)، بهترین عملکرد بهینه‌سازی توپولوژی را نشان داد. براساس نتایج تحلیل حساسیت، عامل‌های انسانی مانند تراکم دام در واحد سطح جنگل (تعداد در هکتار) در کنار عامل‌های طبیعی و اکولوژیکی مانند متوسط قطر درختان توده (سانتی‌متر) و عمق خاک به‌ترتیب بیشترین تأثیر را در میزان انبوهی جنگل نشان دادند. ارزیابی اثرات پروژه‌های اجرا‌شده علاوه‌‌بر اینکه تجربه‌ای در زمینه ارزیابی اثرات توسعه به‌شمار می‌رود، می‌تواند راه‌گشای تصمیم‌گیری در مورد اجرای پروژه‌های مشابه در مکان‌های مشابه باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Modeling of forest canopy density confusion in environmental assessment using artificial neural network

نویسنده [English]

  • Ali Jahani
Assistant Prof., Department of Natural Environment and Biodiversity, Faculty of Environment, University of Environment
چکیده [English]

Environmental Impact Assessment (EIA) is well-known as a basic tool for environmental management and sustainable development. However, modelling approaches are generally preferred when quantitative entities are required for decision-making. The purpose of this study was to test artificial neural network incorporating ecosystem components, forest management activities and the forest canopy density confusion. The study area embraced three districts of Patom, Namkhaneh and Gorazbon within Khyroud research and educational forest of University of Tehran. Land Management Units were formed using available ecological databases and GIS. Based on qualitative and quantitative measures of ecological condition and human activities, the forest canopy density was simulated using artificial neural networks in Neuro Solutions ver. 5 software. Multilayer Perceptron network with one hidden layer and four neurons created the best function for optimizing topology with highest coefficient of determination ~ 0.9864. The results of sensitivity analysis revealed that human activities like the cattle density in land unit (ha), ecological and natural factors such as the average diameter of forest type trees and soil depth are associated with the highest effects on forest canopy density. As a conclusion, the impact assessment of implemented projects could offer an improved solution in decision making for similar projects across similar locations.

کلیدواژه‌ها [English]

  • Environmental Impact Assessment
  • forest canopy density
  • Multilayer perceptron
  • Sensitivity analysis
  • Artificial Neural Network
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