تجزیه‌و‌تحلیل و مدل‌سازی انرژی و میزان تولید گازهای گلخانه‌ای در تولید سیب با به‌کارگیری یادگیری ماشین در شهرستان نظرآباد

نویسندگان

1 1. دانشجو کارشناسی ارشد. مکانیک بیوسیستم انرژی‌های تجدیدپذیر. دانشگاه تهران

2 استاد. مکانیک بیوسیستم انرژی‌های تجدیدپذیر. دانشگاه تهران

3 گروه مهندسی مکانیک ماشین‌های کشاورزی، دانشکده فنی و مهندسی کشاورزی، دانشکده کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران

4 دانشجو دانشگاه پیام نور کرج استان البرز

چکیده

امروزه تأمین امنیت غذایی برای جمعیت روبه‌رشد جهان با حفظ منابع کره زمین و حداقل اثرات زیست‌محیطی به یکی از چالش‌های اساسی و مهم در کشاورزی پایدار تبدیل شده است و استفاده بهینه از منابع یکی از الزامات اصلی کشاورزی پایدار است. در این مطالعه به بررسی الگوی مصرف انرژی در جریان تولید سیب، تجزیه‌وتحلیل و مدل‌سازی انرژی و انتشارات گازهای گلخانه‌ای در شهرستان نظرآباد پرداخته شد. نتایج نشان داد که کل انرژی مصرفی برابر ۴۶/۳۵۹۳۴ مگاژول در هکتار و انتشارات برابر با ۱۲۲۰۰۳۱ گرم معادل کربن‌دی‌اکسید در هکتار بود. کود ازته با سهم ۴۳/۳۲ درصدی از کل انرژی‌های ورودی پرمصرف‌ترین نهاده بود. شاخص‌های کارایی انرژی، بهره‌وری انرژی، شدت انرژی و انرژی خالص به ترتیب ۴۳/۱، (kg/Mj) ۵۹/۰،(Mj/Kg) ۶۷/۱ و (Mj)۱۸/۱۵۵۴۱ به دست آمد. مدل‌سازی با سه روش GBR، DTR و RFR انجام شد و RRMSE به ترتیب ۰۲/۰، ۰۷/۰ و ۰۸/۰ و R به ترتیب ۹۹/۰، ۹۶/۰ و ۹۴/۰ محاسبه شد نتایج نشان داد که روش GBR قادر است بادقت بالاتری مقادیر شاخص‌های بهره‌وری انرژی تولید سیب را پیش‌بینی کند. نتایج نشان داد که مصرف انرژی و انتشارات به‌وسیله نهاده‌های آب آبیاری، الکتریسیته، کودهای شیمیایی و حیوانی، نیروی کارگری، سموم شیمیایی، سوخت دیزل و ماشین‌ها با روش یادگیری ماشین و بادقت بالایی قابل‌پیش‌بینی است. تحلیل حساسیت با SHAP انجام شد و تأثیرگذارترین نهاده روی پیش‌بینی انرژی کود شیمیایی ازته بود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Analysis and modeling of energy and the amount of greenhouse gas production in apple production using machines laerning in Nazarabad city

نویسندگان [English]

  • seyed omid Davodalmosavi 1
  • shahin rafiee 2
  • Ali Jafari 3
  • ali rafiee 4
1 Department of Agricultural Machinery Engineering, University of Tehran, Karaj, Iran
2 Department of Agricultural Machinery Engineering, University of Tehran, Karaj, Iran
3 Department of Mechanical Engineering of Agricultural Machinery, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
4 A student at Payam Noor University in Karaj, Alborz province
چکیده [English]

Today, providing food security for the world's growing population by preserving the earth's resources and minimal environmental effects has become one of the basic and important challenges in sustainable agriculture, and the optimal use of resources is one of the main requirements of sustainable agriculture. In this study, the pattern of energy consumption during apple production, analysis and modeling of energy and greenhouse gas emissions in Nazarabad city was investigated. The results showed that the total energy consumption was equal to 35934.46 megajoules per hectare and the emissions were equal to 1220031 grams of carbon dioxide equivalent per hectare. Nitrogen fertilizer was the most consumed input with a share of 32.43% of the total input energy The indices of energy.efficiency, energy productivity, energy intensity and net energy were obtained as 1.43, (kg/Mj) 0.59, (Mj/Kg) 1.67 and (Mj) 15541.18. Modeling was done with three methods GBR, DTR and RFR and RRMSE was calculated as 0.02, 0.07 and 0.08 and R as 0.99, 0.96 and 0.94 respectively. The results showed that the GBR method is able to is to accurately predict the values of energy efficiency indices of apple production. The results showed that energy consumption and emissions can be predicted by machine learning method with high accuracy through the inputs of irrigation water, electricity, chemical and animal fertilizers, labor force, chemical poisons, diesel fuel and machines. Sensitivity analysis was performed with SHAP and the most influential input on energy prediction was nitrogen fertilizer.

کلیدواژه‌ها [English]

  • Sensitivity analysis with SHAP
  • Nazarabad city
  • energy efficiency
  • apple
  • machine learning
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