Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network
International Journal of Intelligent Information Systems
Volume 3, Issue 6-1, December 2014, Pages: 103-108
Received: Nov. 3, 2014;
Accepted: Nov. 6, 2014;
Published: Nov. 11, 2014
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Farshad Parhizkar Miandehi, Department of Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran
Erfan Zidehsaraei, Department of Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran
Mousa Doostdar, Department of Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran
Iranian ponds and water ecosystems are valuable assets which play decisive roles in economic, social, security and political affairs. Within the past few years, many Iranian water ecosystems such asUrmia Lake, Karoun River and Anzali Pond have been under disappearance threat. Ponds are habitats which cannot be replaced and this makes it necessary to investigate their changes in order to save these valuable ecosystems. The present research aims to investigate and evaluate the trend of variations in Anzali Pond using meteorological data between 1991-2010 by means of GMDH, which is based upon genetic algorithm and is a powerful technique in modeling complex dynamic non-linear systems, and linear regression technique. Input variables of both methodsinclude all factors (inside system and outside system factors) which affect variations in Anzali Pond. Exactness of linear regression method was 78% and exactness of GMDH neural network method was more than 97%. As as result, exactness of GMDH neural network method is significantly better than regression model.
Farshad Parhizkar Miandehi,
Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network, International Journal of Intelligent Information Systems. Special Issue: Research and Practices in Information Systems and Technologies in Developing Countries.
Vol. 3, No. 6-1,
2014, pp. 103-108.
Tavakkoli, B and SabetRaftar, K. investigation of the impact of area, population and population compression factors of water basin on rivers discharging Anzali Pond, journal of environmental studies: special notes on Anzali pond: 51 to 57, 2007.
Zebardast, L, Jafari, H. R, evaluation of the trend of changes in Anzali Pond using remote sensing and presentation of a managerial solution, journal of environmental studies, 57-64, 2011.
Jamalzad, F, determination of the level of sensitivity of different areas of Anzali Pond using GIS, master degree thesis, environment faculty, Tehran University, page 52, 2008.
Ghahraman, A and Attar, F. Anzali Pond in death coma (an ecological-floristic investigation). Journal of environmental studies: special notes on Anzali Pond: 1 to 38.
Abrishami, Hamid and Moeeni, Ali and Mehrara, Mohsen and AHrari, Mahdi and SoleimaniKia, Fatemeh (2008), "modeling and prediction of gasoline price using GMDH neural network", quarterly of Iranian economic studies, 12th year, number 36, pp: 37-58.
Sharzei, Gholam Ali and AHrari, Mahdi and Fakhraee, Hasan (2008), "structural models, time series and GMDH neural network", journal of economic studies, number 84, pp: 151-175.
Abrishami, Hamid and Mehrara, Mohsen and Ahrari, Mahdi and Mir Ghasemi, Soudeh (2009), "modeling and prediction of Iranian economic growth with a GMDH neural network approach", journal of economic studies, number 88, pp: 1-24.
Ozesmi, S. L., E. M., Bauer. “Satellite Remote Sensing of Wetlands. Wetlands Ecology and, Management”, Vol.10, pp.381-402, 2002.
Abbaspour, M. and Nazaridoust, “Determination of Environmental Water Requirements of Lake Urmia, Iran: an Ecological Approach”, International Journal of Environmental Studies, Vol.64, pp.161-169, 2007.
Zhaoning, G., et al. “Using RS and GIS to Monitoring Beijing Wetland Resources Evolution”, IEEE International, Vol.23, pp.4596 – 4599, 2007.
De Roeck, E., Jones, K., “Integrating Remote Sensing and Wetland Ecology: a Case Study on South African Wetlands”, pp.1-5, 2008.
Yung, J.L., “Sustainable Wetland Management Strategies under Uncertainties”, the Environmentalist, Vol.19, pp. 67-79, 2008.
van Stappen, G., Bossier, P., Sepehri, H., Lotfi, V., RazaviRouhani, S., Sorgeloos, P., “Effects of Salinity on Survival,Growth, Reproductive and Life Span Characteristics of Artemia Populations from Urmia Lake and Neighboring Lagoons”, Journal of Biological Sciences, Vol.11, pp.164-172, 2008.
Howland. J.C, Voss. M.S. “Natural Gas Prediction Using the Group Method of Data Handling”, ASC. . (2003)
Ivakhnenko.G.A (1995),”The Review of Problems Solvable by Algorithms of the Method of Data Handling (GMDH)”, Pattern Recognition and Image Analysis, Vol.5, No.4, PP 527-535.
Ivakhnenko. G.A and Muller. J.A. (1996). “Recent Development of Self-Organizing Modeling in Prediction and Analysis of Stock Market”, Available in URL Address: http://www.inf.kiev.ua/GMDH Home/Articles.
Ahmadi, R., Mohebbi, F., Hagigi, P., Esmailly, L., Salmanzadeh, R. Macro-invertebrates in the Wetlands oftheZarrineh "estuary at the south of Urmia Lake. International Journal of Environmental Restoration", 5(4), 1047-1051. (2011).