Farajzadeh M * ¹ (PhD), Darand M² (MS)

  ¹ Department of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Iran

  ² Departmant of Geography, Tarbiat Modares University, Tehran, Iran

  Received: 8 Jun 2009, Accepted: 3 Nov 2009

  Abstract

Introduction: Seasonal and daily human mortality changes have correlation with air temperature. In this research, daily human mortality data and air temperature during 2002- 2005 has been used.

Methods: For data analysis, Pearson adjusted correlation coefficient, polynomial regression as a semi-linear method and artificial neural network as a non-linear method have been used.

Results: The results of Pearson correlation analysis showed significant negative correlation between air temperature and total human mortality and mortality caused by cardiovascular diseases. Their correlation by artificial neural network and genetic algorithm indicated a better result compared to the classic methods (linear and polynomial regression). After network training with different hidden layers and different stepsizes, it was indicated that the use of artificial neural network with one hidden layer of perceptron results in a better model, in the setting of arranged samples.

Conclusion: Therefore, it can be said that neural network can forecast the nonlinear relation between monthly mortality and air temperature, while the combined model of neural network with genetic algorithms can increase analysis speed and accuracy and therefore decrease errors in calculations.

Key words: Mortality, Tehran, Artificial Neural Network, Regression Analysis, Temperature.

Hakim Research Journal 2009 12(3): 45- 53.

  * Corresponding Author: Department of Remote Sensing and GIS, Tarbiat Modares University, P.O. Box 14155-4838, Tehran, Iran. Tel: +98- 912- 1723124, Fax: +98- 21- 88006544, Email: farajzam@modares.ac.ir�