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

- 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 7^th 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 3^rd 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).