Area and Depth Investigation of Anzali Pond Using Satellite Imageries and Group Method of Data Handling Neural Network
International Journal of Intelligent Information Systems
Volume 3, Issue 6-1, December 2014, Pages: 67-70
Received: Oct. 21, 2014; Accepted: Oct. 23, 2014; Published: Oct. 31, 2014
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Authors
Farshad Parhizkar Miandehi, Electronic and Computer Faculty, Islamic Azad University of Zanjan, Zanjan, Iran
Asadollah Shahbahrami, Engineering faculty, University of Guilan, Rasht, Iran
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Abstract
Analysis of changes in natural resources is one of the fundamental issues in remote sensing. Several research studies regarding the process of changes in natural resources using satellite imageries and image processing techniques have been done. Anzali pond is one of the important ecosystems in Iran that under the impact of some factors such as drought has the gradual drying trend over the last years. This study measures the area of basin surface and predicts the process of changes in the climate of the pond neighborhood during the next years, using GMDH neural network. Satellite imagery and meteorological data is used for this analysis. The final results represent reduction in area from 82 km^2 in 1998 to 51 km^2 in 2010. The average depth of the pond decreased to less than 4m in 2010 from 9m in 1998. The main reason for this reduction is diversion of rivers, sediment entering and changes in land use around the pond. If this trend continues, the amount of pollutants and toxins will reach to warning and this is a serious threat for animals and pond dwellers.
Keywords
Anzali Pond, Remote Sensing, Image Processing, GMDH Neural Network
To cite this article
Farshad Parhizkar Miandehi, Asadollah Shahbahrami, Area and Depth Investigation of Anzali Pond Using Satellite Imageries and Group Method of Data Handling 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. 67-70. doi: 10.11648/j.ijiis.s.2014030601.22
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