Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network

Farshad Parhizkar Miandehi; Erfan Zidehsaraei; Mousa Doostdar

doi:doi:10.11648/j.ijiis.s.2014030601.29

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Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network

Farshad Parhizkar Miandehi, Erfan Zidehsaraei, Mousa Doostdar

Published in International Journal of Intelligent Information Systems (Volume 3, Issue 6-1)

Received: 3 November 2014 Accepted: 6 November 2014 Published: 11 November 2014

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Abstract

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.

Published in	International Journal of Intelligent Information Systems (Volume 3, Issue 6-1) This article belongs to the Special Issue Research and Practices in Information Systems and Technologies in Developing Countries
DOI	10.11648/j.ijiis.s.2014030601.29
Page(s)	103-108
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Anzali Pond, Regression Analysis, GMDH Neural Network

References

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[2]	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.
[3]	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.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	Ozesmi, S. L., E. M., Bauer. “Satellite Remote Sensing of Wetlands. Wetlands Ecology and, Management”, Vol.10, pp.381-402, 2002.
[9]	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.
[10]	Zhaoning, G., et al. “Using RS and GIS to Monitoring Beijing Wetland Resources Evolution”, IEEE International, Vol.23, pp.4596 – 4599, 2007.
[11]	De Roeck, E., Jones, K., “Integrating Remote Sensing and Wetland Ecology: a Case Study on South African Wetlands”, pp.1-5, 2008.
[12]	Yung, J.L., “Sustainable Wetland Management Strategies under Uncertainties”, the Environmentalist, Vol.19, pp. 67-79, 2008.
[13]	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.
[14]	Howland. J.C, Voss. M.S. “Natural Gas Prediction Using the Group Method of Data Handling”, ASC. . (2003)
[15]	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.
[16]	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.
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APA Style

Farshad Parhizkar Miandehi, Erfan Zidehsaraei, Mousa Doostdar. (2014). Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network. International Journal of Intelligent Information Systems, 3(6-1), 103-108. https://doi.org/10.11648/j.ijiis.s.2014030601.29

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ACS Style

Farshad Parhizkar Miandehi; Erfan Zidehsaraei; Mousa Doostdar. Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network. Int. J. Intell. Inf. Syst. 2014, 3(6-1), 103-108. doi: 10.11648/j.ijiis.s.2014030601.29

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AMA Style

Farshad Parhizkar Miandehi, Erfan Zidehsaraei, Mousa Doostdar. Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network. Int J Intell Inf Syst. 2014;3(6-1):103-108. doi: 10.11648/j.ijiis.s.2014030601.29

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@article{10.11648/j.ijiis.s.2014030601.29,
  author = {Farshad Parhizkar Miandehi and Erfan Zidehsaraei and Mousa Doostdar},
  title = {Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network},
  journal = {International Journal of Intelligent Information Systems},
  volume = {3},
  number = {6-1},
  pages = {103-108},
  doi = {10.11648/j.ijiis.s.2014030601.29},
  url = {https://doi.org/10.11648/j.ijiis.s.2014030601.29},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2014030601.29},
  abstract = {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.},
 year = {2014}
}

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TY  - JOUR
T1  - Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network
AU  - Farshad Parhizkar Miandehi
AU  - Erfan Zidehsaraei
AU  - Mousa Doostdar
Y1  - 2014/11/11
PY  - 2014
N1  - https://doi.org/10.11648/j.ijiis.s.2014030601.29
DO  - 10.11648/j.ijiis.s.2014030601.29
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 103
EP  - 108
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.s.2014030601.29
AB  - 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.
VL  - 3
IS  - 6-1
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Farshad Parhizkar Miandehi, Erfan Zidehsaraei, Mousa Doostdar. (2014). Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network. International Journal of Intelligent Information Systems, 3(6-1), 103-108. https://doi.org/10.11648/j.ijiis.s.2014030601.29

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ACS Style

Farshad Parhizkar Miandehi; Erfan Zidehsaraei; Mousa Doostdar. Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network. Int. J. Intell. Inf. Syst. 2014, 3(6-1), 103-108. doi: 10.11648/j.ijiis.s.2014030601.29

Copy | Download

AMA Style

Farshad Parhizkar Miandehi, Erfan Zidehsaraei, Mousa Doostdar. Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network. Int J Intell Inf Syst. 2014;3(6-1):103-108. doi: 10.11648/j.ijiis.s.2014030601.29

Copy | Download

@article{10.11648/j.ijiis.s.2014030601.29,
  author = {Farshad Parhizkar Miandehi and Erfan Zidehsaraei and Mousa Doostdar},
  title = {Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network},
  journal = {International Journal of Intelligent Information Systems},
  volume = {3},
  number = {6-1},
  pages = {103-108},
  doi = {10.11648/j.ijiis.s.2014030601.29},
  url = {https://doi.org/10.11648/j.ijiis.s.2014030601.29},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2014030601.29},
  abstract = {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.},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Modeling and Prediction of Changes in Anzali Pond Using Multiple Linear Regression and Neural Network
AU  - Farshad Parhizkar Miandehi
AU  - Erfan Zidehsaraei
AU  - Mousa Doostdar
Y1  - 2014/11/11
PY  - 2014
N1  - https://doi.org/10.11648/j.ijiis.s.2014030601.29
DO  - 10.11648/j.ijiis.s.2014030601.29
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 103
EP  - 108
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.s.2014030601.29
AB  - 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.
VL  - 3
IS  - 6-1
ER  -

Copy | Download