The Modeling and Comparison of GMDH and RBF Artificial Neural Networks in Forecasting Consumption of Petroleum Products in the Agricultural Sector

Document Type: Research Paper


1 Faculty of Economic, Chabahar University, Chabahar, Iran

2 Department of Economic, University of Sistsn and Baluchestan, Zahedan,Iran


Energy plays a significant role in today's developing societies. The role of energy demands to make decisions and policy with regard to its production, distribution, and supply. The vital importance of energy, especially fossil fuels, is a factor affecting agricultural production.
This factor has a great influence on the production of agricultural products in Iran. The forecast of the consumption of oil products by the agricultural sector can help managers and planners to adopt sound management practices for their consumption. Presently, artificial neural networks are regarded as a powerful tool for the analysis and modeling of nonlinear relationships. The present study employed GMDH and RBF artificial neural networks to estimate the consumption of oil products by the agricultural sector. The underpinning parameters were selected to include the value added to the fixed price, rural population, agricultural land area, agricultural mechanization (tractor), and the consumption rate of oil products, electricity, price of oil products, and total energy use by the agricultural sector for the period of 1967-2017. The comparison of MSE, MAE, and MAPE for the GMDH and RBF models showed that the GMDH neural network was highly capable of modeling the energy consumption of the agricultural sector.


Article Title [Persian]

مدل سازی و مقایسه شبکه های عصبی مصنوعی GMDH و RBF در پیش بینی مصرف فرآورده های نفتی در بخش کشاورزی

Authors [Persian]

  • مجتبی عباسیان 1
  • علی سردار شهرکی 2
  • جواد شهرکی 2
1 گروه اقتصاد، دانشگاه علوم دریایی، چابهار،ایران
2 گروه اقتصاد، دانشگاه سیستان و بلوچستان، زاهدان، ایران.
Abstract [Persian]

انرژی در جوامع در حال توسعه نقش مهمی دارد. نقش تقاضای انرژی در تصمیم گیری و سیاست گذاری بر تولید، توزیع و عرضه آن و اهمیت حیاتی انرژی، به ویژه سوخت های فسیلی، یک عامل موثر بر تولید کشاورزی است. این عامل تأثیر زیادی بر تولید محصولات کشاورزی در ایران دارد. پیش بینی مصرف محصولات نفتی توسط بخش کشاورزی می تواند به مدیران و برنامه ریزان کمک کند تا شیوه های مدیریت مناسب برای مصرف خود را به کار گیرند. در حال حاضر شبکه های عصبی مصنوعی به عنوان یک ابزار قدرتمند برای تحلیل و مدل سازی روابط غیر خطی در نظر گرفته می شوند. در این تحقیق، شبکه های عصبی مصنوعی GMDH و RBF به منظور تخمین مصرف محصولات نفتی توسط بخش کشاورزی مورد استفاده قرار گرفت. پارامترهای پایه ای شامل ارزش افزوده به قیمت ثابت، جمعیت روستایی، مساحت زمین های کشاورزی، مکانیزاسیون کشاورزی (تراکتور) و میزان مصرف محصولات نفتی، برق، قیمت محصولات نفتی و مصرف انرژی کل کشاورزی بخش برای دوره 1967-2017 انتخاب شدند. مقایسه MSE، MAE و MAPE برای مدلهای GMDH و RBF نشان داد که شبکه عصبی GMDH توانایی بالایی در مدل کردن مصرف انرژی بخش کشاورزی دارد.

Keywords [Persian]

  • شبکه های عصبی مصنوعی
  • نفت
  • عملکرد پایه شعاعی (RBF)
  • GMDH
  • بخش کشاورزی


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