بررسی و پیش‌بینی میزان بخش‌های اثر زیست‌محیطی در پرورش ماهیان گرم‌آبی استان گیلان با استفاده از روش‌ سامانه‌ی استنتاج عصبی-فازی تطبیقی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار گروه مهندسی ماشین‌های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج.

2 دانشجوی دکتری مکانیزاسیون کشاورزی، دانشکده مهندسی زراعی و عمران روستایی، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان، اهواز

3 استادیار گروه مهندسی ماشین‌های کشاورزی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج

چکیده

در سال­های اخیر ارزیابی چرخه حیات به ابزار مناسبی جهت بررسی و تعیین میزان اثرات زیست­محیطی در تولیدات کشاورزی و صنایع غذایی تبدیل گردیده است؛ به طوری که در بسیاری از کشورها از آن به عنوان ابزاری برای تصمیم­گیری­های کلان در برنامه­­ریزی تولیدات کشاورزی استفاده می­شود. با در نظر گرفتن ماهی به‌ عنوان تأمین­کننده­ی بخش مهمی از پروتئین مورد نیاز بشر، پژوهشی بر روی بررسی شاخص­های زیست­محیطی (بخش­های اثر) در سامانه­ی تولید ماهیان ‌‌‌‌‌گرم­آبی در استان گیلان انجام گرفت. اطلاعات مربوط به میزان تولید نهاده­های مصرفی (انتشارات غیر مستقیم) و مصرف آن­ها در استخرها (انتشارات مستقیم) از طریق 57 پرسش‌نامه‌­ی نمونه­گیری شده و پایگاه داده­ای اکواینونت جمع­آوری گردید. نتایج نرمال­سازی بخش­های اثر  نشان داد که شاخص‌های مسمومیت آب­های آزاد، اسیدی شدن و مسمومیت آب­های سطحی بیشترین مقادیر آلاینده­های زیست­محیطی را به ترتیب با مقادیر 7-10×17/5، 7-10×95/1، 7-10×98/0 به خود اختصاص داد­ه­اند. انتشارات ناشی از تولید نهاده‌ی الکتریسیته (انتشارات غیر مستقیم) و آلاینده­های منتشر شده از مصرف سه نهاده­ی الکتریسیته، کودهای شیمیایی و کود دامی (انتشارات مستقیم) بیشترین سهم از میزان آلایندگی را بر روی شاخص­های مذکور داشتند. همچنین مقایسه­ی نتایج روش­های طراحی انفیس نشان داد که روش خوشه‌بندی فازی 8 خوشه‌ای­ نسبت به روش­های جداسازی شبکه­ای و خوشه­بندی کاهشی، با دقت بالاتر و خطای کمتری قادر به پیش­بینی مقادیر بخش­های اثر زیست­محیطی می­باشد. 

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigating and Predicting the Amount of Environmental Impact in Breeding Warm Water Fish in Guilan Province using Comparative Neuro-Fuzzy Inductive Inference System

نویسندگان [English]

  • Asadollah Akram 1
  • behzad elhami 2
  • Majid Khanali 3
1 Associate Prof., Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran.
2 PhD. Student of Agricultural Mechanization, Department of Agricultural Machinery Engineering, Agriculture Science and Natural Resources University of Ahvaz
3 Assistant Prof., Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran.
چکیده [English]

In recent years, life cycle assessment (LCA) approach is turned to be a useful tool for investigating and determining the environmental impacts of agricultural products and food industry, so that in most countries, it is used as a tool for decision-making in agricultural production planning. Considering the fish as an important part of the human protein required, an investigation was carried out on the environmental indicators (impact categories) in the system of warm water production in Guilan province. Data related to the production value of inputs (indirect emissions) and their’s consumption (direct emissions) in ponds were collected using sampled questionnaire and Ecoinvent database. The results of normalization showed that marine aquatic ecotoxicity (MAET), acidification (AC) and Freshwater Aquatic Ecotoxicity (FAET) have the highest amount of environmental pollutants as 5.17×10-7, 1.95×10-7 and 0.98×10-7, respectively. Emissions resulting from the production of electricity (direct emissions) and pollutants released from the use of electricity, chemical fertilizers and manure (indirect emissions) have the highest share of pollution on these indicators. Also, the comparison of the results of ANFIS design methods showed that the fuzzy C-means method with 8 clusters, with higher accuracy and less error, was able to predict the values of environmental impact categories.

کلیدواژه‌ها [English]

  • Life Cycle Assessment
  • fuzzy C-meansو Final Environmental Index
  • Marine Aquatic Ecotoxicity
Abeliotis, K., Detsis, V. and Pappia, C. (2013). Life cycle assessment of bean production in the Prespa National Park, Greece. Journal of Cleaner Production, 41, 89-96.
Ahmed, S.h., Rahman, A.F.M., Ghulam-Mustafa, M.D., Belal-Hossain, M., Nahar, N. (2014). Nutrient Composition of indigenous and exotic fishes of rainfed Waterlogged Paddy Fields in Lakshmipur, Bangladesh. Word Journal of Zoology, 7(2), 135-140.
Anonymous (2003). PRé Consultants. SimaPro 5 Database Manual.
Anonymous (2010). Database EcoInvent version 2 (www.ecoinvent.org).
Anonymous (2017). Iran Fisheries Organization [In Persian] (http://shilat.com/site/vahed).
Aubin, J., Papatryphon, E., Vander Werf, H.M.G. and Chatzifotis, S. (2009). Assessment of the environmental impact of carnivorous finfish production systems using life cycle assessment. Journal of Cleaner Production, 17, 354–361.
Brentrup, F., Küsters, J. Kuhlmann, H. and Lammel, J. (2004). Environmental impact assessment of agricultural production systems using the life cycle assessment methodology: I. Theoretical concept of a LCA method tailored to crop production. European Journal of Agronomy, 20, 247–264.
Chen, X., Samson, S., Tocqueville, A. and Aubin, J. (2015). Environmental assessment of trout farming in France by life cycle assessment: using bootstrapped Principal Component Analysis to better define system classification. Journal of Cleaner Production, 87, 87-95.
Chiu, S.L. (1994) Fuzzy model identification based on cluster estimation.  Journal of Intelligent & Fuzzy Systems, 2,267–278.
Cochran, W.G. (1977). Sampling Techniques. Third Edition. P.135.
Dunn, J.C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3,32–57          
Elhami, B., Akram, A.  and Khanali, M. (2016). Optimization of energy consumption and environmental impacts of chickpea production using data envelopment analysis (DEA) and multi objective genetic algorithm (MOGA) approaches. Information Processing in Agricuture, 3(3), 190-205.
Ewoukem, T., Aubin, J., Mikolasek,  M.S., Corson,  M., Tomedi-Eyango, J., Tchoumboue, H.M.G., Vander Werf, D. and Ombredane, D. (2012) Environmental impacts of farms integrating aquaculture and agriculture in Cameroon. Journal of Cleaner Production, 28, 208-214.
FAO. (2014). FAO Yearbook of fishery statistics 2014. Food and Agriculture Organization of the United Nations Rome, 42-58.
Fantin, V., Righi, S., Rondini, I. and Masoni, P. (2016). Environmental assessment of wheat and maize production in an Italian farmers' cooperative. Journal of Cleaner Production, 140(2), 1-13.
Forchino, A. A., Lourguioui, H., Brigolin, D. and Pastres, R. (2016). Aquaponics and sustainability: the comparison of two different aquaponic techniques using the Life Cycle Assessment (LCA).Aquacultural Engineering, 77, 80-88.
Guinée, J. B. (2002). Handbook on life cycle assessment operational guide to the ISO standards. The International Journal of Life Cycle Assessment, 7(5), 311-313.
Guinée, J.B., Heijungs, R., Huppes, G., Zamagni, A., Masoni, P., Buonamici, R., Ekvall, T. and Rydberg T. (2011). Life cycle assessment: past, present, and future. Environmental Science Technology, 45(1), 90–96.
IPCC. (2006). Guidelines for national greenhouse gas inventories. In: Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K. (Eds.), Prepared by the National Greenhouse Gas Inventories Programme. IGES, Japan (www.ipccnggip.iges.or)
ISO 14040. (2006). Environmental Management Life Cycle Assessment Principles and Framework. International Journal of Life Cycle Assessment, 11(2), 36 p.
Jang, S.R., Sunm, T. and  Mizutani, E. (1997). Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall Inc, Upper Saddle River. <http://papers.cumincad.org/cgi-bin/works/Show?d036>.
Khoshnevisan, B., Rafiee, S., Omid, M., Mousazadeh, H., and Sefeedpari, P. (2013a). Prognostication of environmental indices in potato production using artificial neural networks. Journal of Cleaner Production, 52, 402–409.
Khoshnevisan, B., Rafiee, S. and Mousazadeh, H. (2013b). Environmental impact assessment of open field and greenhouse strawberry production. European Journal of Agronomy, 50, 29-37.
Khoshnevisan, B., Rafiee, S. and Mousazadeh, H. (2014). Application of multi-layer adaptive neuro-fuzzy inference system for estimation of greenhouse strawberry yield. Measurement, 47, 903-910.
Khoshnevisan, B., Bolandnazar, E., Shamshirband, S., Motamed Shariati, H., Anvar,N.B. and Mat Kiah, M.S. (2015a). Decreasing environmental impacts of cropping systems using life cycle assessment (LCA) and multi-objective genetic algorithm. Journal of Cleaner Production, 86, 67–77.
Khoshnevisan, B., Rafiee, S., Omid, M., Mousazadeh, H., Shamshirband S. and AbHamid, S.H. (2015b). Developing a fuzzy clustering model for better energy use in farm management systems.  Renewable and Sustainable Energy Reviews, 48, 27–34.
Lopez- Andres, J.J., Aguilar-Lasserre, A.A., Morales-Mendoza, L.F., Azzaro-Pantel, C., Perez-Gallardo, J.R. and Rico-Contreras, J.O. (2018). Environmental impact assessment of chicken meat production via an integrated methodology based on LCA, simulation and genetic algorithms. Journal of Cleaner Production, 174, 477-491.
V.Medeiros, M., Aubin, J. and Camargo, A. (2017). Life cycle assessment of fish and prawn production: Comparison of monoculture and polyculture freshwater systems in Brazil. Journal of Cleaner Production, 156, 528-537.
Mousavi-Avval, S.H., Rafiee, S., Sharifi, M., Hosseinpour, S. and Shah, A. (2017). Combined application of Life Cycle Assessment and Adaptive Neuro-Fuzzy Inference System for modeling energy and environmental emissions of oilseed production. Renewable and Sustainable Energy Reviews, 78, 807–820.
Nabavi-Pelesaraei, A., Rafiee, S. and Mohtasebi, S.S. (2018). Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production. Science of the Total Environment, 631,1279–1294.
Naderloo, L., Alimardani, R., Omid, M., Sarmadian, F., Javadikia, P. and Torabi, M.Y. (2012). Modeling of wheat yield and sensitivity analysis based on energy inputs for three year in Abyek town, Ghazvin, Iran. Agricultural Engineering International, 15(1), 68-77.
Nemecek, T. and Kagi, T. (2007). Life cycle inventories of agricultural production systems. Ecoinvent report No. 15, Dübendorf, CH: Swiss Centre for Life Cycle Inventories (www. EcoInvent.org/ documentation/reports).
Nemecek, T., Dubois, D., Huguenin-Elie, O. and Gaillard, G. (2011). Life cycle assessment of Swiss farming systems: I. Integrated and organic farming. Agricultural Systems, 104(3), 217-232.
Nguyen, T. L. T., & Hermansen, J. E. (2012). System expansion for handling co-products in LCA of sugar cane bio-energy systems: GHG consequences of using molasses for ethanol production. Applied energy, 89(1), 254-261.
Nikkhah, A., Khojastehpour, M., Emadi, B., Taheri-Rad, A. and Khorramdel, S. (2015).  Environmental impacts of peanut production system using life cycle assessment methodology. Journal of Cleaner Production, 92, 84-90.
Pahlavan, R., Omid, M. and Akram, A. (2012). Energy input-output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy, 37(1), 171–176.
Phong, L.T., de Boer, I.J.M. and Udo, H.M.J. (2011). Life cycle assessment of food production in integrated agriculture-aquaculture systems of the Mekong Delta. Livestock Science, 139, 80-90.
Rafiee, S., Khoshnevisan, B., Mohammadi, I., Aghbashlo, M., Mousazadeh, H. and Clark, S. (2016) Sustainability evaluation of pasteurized milk production with a life cycle assessment approach: An Iranian case study. Science of the Total Environment, 562, 614–627.
Rebitzer, G., Ekvall, T., Frischknecht, R., Hunkeler, D., Norris, G., Rydberg, T., Schmidt, W.P., Suh, S.,
Rezaei, E., Karami, A., Yousefi, T. and Mahmoudinezhad, S. (2012). Modeling the free convection heat transfer in a partitioned cavity using ANFIS. International Communications in Heat and Mass Transfer, 39(3), 470-475.
D’Orbcastel, E., Blancheton, J.P. and Aubin, J. (2009). Towards environmentally sustainable aquaculture: comparison between two trout farming systems using Life Cycle Assessment. Aquaculture Engineering, 40(3), 113-119.
Safa, M. and Samarasinghe, S. (2011). Determination and modeling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”. Energy, 36, 5140-5147.
Salehi, H (1999). Strategic analyses of carp culture development in Iran, Ph.D. Theses. 328 p [In Persian].
Sahle, A. and Potting J. (2013). Environmental life cycle assessment of Ethiopian rose cultivation. Science of Total Environment, 443, 163–172.
Shamshirband, S., Khoshnevisan, B., Yousefi, M., Bolandnazar, E., Anuar, N.B, Abdol Wahab, A.W. and Rehman Khan, S.U. (2015) A multiobjective evolutionary algorithm for energy management of agricultural systems – a case study in Iran. Renewable Sustainable Energy Reviews, 44, 457–465.
Thévenot, A., Aubin, J., Tillard, E. and Vayssières, J. (2013). Accounting for farm diversity in Life Cycle Assessment studies- the case of poultry production in a tropical island. Journal of Cleaner Production, 57, 280-292.
Thrane, M (2006). Environmental impacts from Danish fish products. Department of Development and Planning, Aalborg University of Denmark, pp. 535.
Vagnoni, E., Franca, A., Breedveld, L., Porqueddu, C., Ferrara, R. and Duce, P. (2014). Environmental footprint of milk production from Mediterranean sheep systems. In Proceedings of the 9th International Conference on Life Cycle Assessment in the Agri-Food Sector (LCA Food), San Francisco, California, USA, October, 2014, pp. 1408-1417.
Vander Werf, H.M.G., Petit, J. and Sanders, J. (2005). The environmental impacts of the production
of concentrated feed: the case of pig feed in Bretagne. Agricultural System, 83(2), 153–177.