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 and Md. Shahjahan
DOI : 10.3844/ajassp.2013.1172.1180
American Journal of Applied Sciences
Volume 10, Issue 10
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.
© 2013 Abu Nasir Mohammad Enamul Kabir, Hussain Muhammad Imran Hasan, Mohd Abdur Rashid, Azralmukmin Azmi, Md. Zakir Hossain and Md. Shahjahan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.