Resemblance of rain fall in Bangladesh with correlation dimension and neural network learning

Abu Nasir Mohammad Enamul Kabir, Hussain Muhammad Imran Hasan, Mohd Abdur Rashid, Azralmukmin Azmi, Md Zakir Hossain, Md Shahjahan

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1172-1180
Number of pages9
JournalAmerican Journal of Applied Sciences
Volume10
Issue number10
DOIs
Publication statusPublished - 10 Oct 2013
Externally publishedYes

Fingerprint

Dive into the research topics of 'Resemblance of rain fall in Bangladesh with correlation dimension and neural network learning'. Together they form a unique fingerprint.

Cite this