| تعداد نشریات | 284 |
| تعداد نشریات سازمان | 82 |
| تعداد شماره‌ها | 3,081 |
| تعداد مقالات | 34,377 |
| تعداد مشاهده مقاله | 48,022,713 |
| تعداد دریافت فایل اصل مقاله | 78,931,743 |
مدل‌سازی آشفتگی انبوهی جنگل در ارزیابی محیطی با استفاده از شبکه عصبی مصنوعی | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 12، دوره 24، شماره 2 - شماره پیاپی 64، تیر 1395، صفحه 322-310 اصل مقاله (384.54 K) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2016.106998 | ||
| نویسنده | ||
| علی جهانی* | ||
| 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 | ||
| ارزیابی اثرات محیط زیستی؛ انبوهی جنگل؛ پرسپترون چندلایه؛ تحلیل حساسیت؛ شبکه عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Modeling of forest canopy density confusion in environmental assessment using artificial neural network | ||
| نویسندگان [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 | ||
| مراجع | ||
|
- Aghajani, H., Marvie Mohadjer, M.R., Jahani, A., Asef, M.R., Shirvany, A. and Azaryan, M., 2014. Investigation of affective habitat factors affecting on abundance of wood macrofungi and sensitivity analysis using the artificial neural network. Iranian Journal of Forest and Poplar Research, 21(4): 9-19 (In Persian). - Arsene, C.T.C., Gabrys, B. and Al-Dabass, D., 2012. Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection. Expert Systems with Applications, 39: 13214-13224. - Brokaw, N. and Busing, R., 2000. Niche Versus Chance and Tree Diversity in Forest Gaps. Published by United States Forestry Sciences Laboratory, New York, 183p. - Collet, C., Lanter, O. and Pardos, M., 2001. Effects of canopy opening on height and diameter growth in naturally regenerated beech seedlings. Journal of Annals of Forest Science, 58(2): 127-134. - Dupuy, J.M. and Chazdon, R.L., 2008. Interaction effects of canopy gap, understory vegetation and leaf litter on tree seedling recruitment and composition in tropical secondary forests. Forest Ecology and Management, 255(1): 3716-3725. - Eccleston, C.H., 2000. Environmental Impact Assessment. John Wiley & Sons, New York, 346p. - Fernandez, F., Seco, J., Ferrer, A. and Rodrigo, M.A., 2009. Use of neuro fuzzy networks to improve wastewater flow-rate forecasting. Environmental Modelling & Software, 24: 686-693. - George, C., 1999. Testing for sustainable development through environmental assessment. Environmental Impact Assessment Reviews, 19: 175-200. - Gumus, S., Acar, H.H. and Toksoy, D., 2008. Functional Forest Road Network Planning by Consideration of Environmental Impact Assessment for Wood Harvesting. Journal of Environmental Monitoring and Assessment, 142: 109-116. - Hanna, K.S., Polonen, I. and Raitio, K., 2011. A potential role for EIA in Finnish forest planning: learning from experiences in Ontario, Canada. Journal of Impact Assessment and Project Appraisal, 29(2): 99-108. - Iliadis, L.S. and Maris, F., 2007. An artificial neural network model for mountainous water-resources management: the case of Cyprus mountainous watersheds. Environmental Modelling & Software, 22: 1066-1072. - Jahani, A., Feghhi, J., Makhdoum, M.F. and Omid, M., 2016. Optimized forest degradation model (OFDM): an environmental decision support system for environmental impact assessment using an artificial neural network. Journal of Environmental Planning and Management, 59(2): 222-244. - Kathke, S. and Bruelheide, H., 2010. Gap dynamics in a near-natural spruce forest at Mt. Brocken, Germany. Forest Ecology and Management, 259(1): 624-632. - Knowler, D. and Lovett, J., 1996. Manual for Environmental Assessment in Forestry. Published by Department of Environmental Economics and Environmental Management, University of York, 108p. - Koskela, M., 2011. Expert views on environmental impacts and their measurement in the forest industry. Journal of Cleaner Production, 19: 1365-1376. - Leknes, E., 2001. The role of EIA in decision-making process. Environmental Impact Assessment Reviews, 21: 309–334. - Maier, H., Jain, R.A., Dandy, G.C. and Sudheer, K.P., 2010. Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environmental Modelling & Software, 25(8): 891-909. - Maier, H.R. and Dandy, G.C., 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling & Software, 15(1): 101-124. - Makhdoum, M.F., 2002. Degradation model: a quantitative EIA instrument, acting as a decision support system (DSS) for environmental management. Environmental Management, 30(1): 151-156. - Mayo, J., 2002. Dead trees effects in forest ecosystem. Science Finding Journal, 11(1): 25-34. - Michelsen, O., Solli, C. and Stromman, A.H., 2008. Environmental Impact and Added Value in Forestry Operations in Norway. Journal of Industrial Ecology, 12(1): 69-81. - Oghnom, M., 2011.Environment impact assessment of forestry plan using degradation model and matrix. M.Sc. thesis, Faculty of Natural Resources, University of Tehran, Karaj, 183p (In Persian). - Tayebi, M.H., Tangestani, M.H. and Roosta, H., 2010. Environmental impact assessment using neural network model: A case study of the Jahani, Konarsiah and Kohe Gach salt plugs, Shiraz, Iran. Abstracts of the 7th ISPRS TC VII Symposium. Austria, 18-21 Aug. 2010: 15-18. - Vali, A., Ramesht, M.H., Seif, A. and Ghazavi, R., 2012. An assessment of the artificial neural networks technique to geomorphologic modeling sediment yield (case study Samandegan river system). Geography and Environmental Planning Journal, 44(4): 5-9. - Wang, X.D., Zhong, X.H., Liu, S.Z., Liu, J.G., Wang, Z.Y. and Li, M.H., 2008. Regional assessment of environmental vulnerability in the Tibetan Plateau: development and application of a new method. Journal of Arid Environments, 72: 1929-1939. - Yijun, L., Jiali, T., Hongfen, J., Guangping, Z. and Zhimin, Y., 2010. Artificial neural networks applied in environmental quality assessment. Abstracts of the 3rd IEEE International Computer Science and Information Technology Symposium. Spain, 12-15 Sep. 2010: 19-22. - Zolfaghari, E., Marvi Mohajer, M.R., Zahedi Amiri, Gh. and Namiranian, M., 2011. Investigation of forest crown gap effects on rehabilitation and diversity of natural regeneration settlement (Case study, Chelir district from Kheiroud forest, Nooshahr). Journal of Forest Science and Engineering, 1(2): 24-28 (In Persian). | ||
|
آمار تعداد مشاهده مقاله: 1,271 تعداد دریافت فایل اصل مقاله: 1,119 |
||