Research Article Open Access

Resemblance of Rain Fall in Bangladesh with Correlation Dimension and Neural Network Learning

Abu Nasir Mohammad Enamul Kabir1, Hussain Muhammad Imran Hasan1, Mohd Abdur Rashid2, Azralmukmin Azmi2, Md. Zakir Hossain1 and Md. Shahjahan1
  • 1 Khulna University of Engineering and Technology, Bangladesh
  • 2 University Malaysia Perlis, Malaysia
American Journal of Applied Sciences
Volume 10 No. 10, 2013, 1172-1180

DOI: https://doi.org/10.3844/ajassp.2013.1172.1180

Submitted On: 12 June 2013 Published On: 5 September 2013

How to Cite: Kabir, A. N. M. E., Hasan, H. M. I., Rashid, M. A., Azmi, A., Hossain, M. Z. & Shahjahan, M. (2013). Resemblance of Rain Fall in Bangladesh with Correlation Dimension and Neural Network Learning. American Journal of Applied Sciences, 10(10), 1172-1180. https://doi.org/10.3844/ajassp.2013.1172.1180

Abstract

Rain fall and Temperature are undoubtedly two important factors that balance water in the environment. Adequate study of the rain behavior helps to forecast it. The time series obtained from different stations of the country throughout the several years are collected and analyzed. The dynamics of rain fall time series is analyzed with Correlation Dimension (CD) to characterize the several zones of Bangladesh. In addition a Neural Network (NN) predictor model was designed to realize complexity of rain fall. We found the interesting similarity between CD and NN predictor. The findings are useful in explaining why several zones show behavioral regularity and change.

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Keywords

  • Rain Fall
  • Time Series Analysis
  • Complexity
  • Neural Network
  • Learning and Prediction