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Reseach Article

Neural Network based Electrostatic Fields Distribution Modeling for Harmattan Season in Zaria, Nigeria

by Akinsanmi O., Ekundayo K. R.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 10
Year of Publication: 2013
Authors: Akinsanmi O., Ekundayo K. R.
10.5120/13287-0891

Akinsanmi O., Ekundayo K. R. . Neural Network based Electrostatic Fields Distribution Modeling for Harmattan Season in Zaria, Nigeria. International Journal of Computer Applications. 76, 10 ( August 2013), 39-45. DOI=10.5120/13287-0891

@article{ 10.5120/13287-0891,
author = { Akinsanmi O., Ekundayo K. R. },
title = { Neural Network based Electrostatic Fields Distribution Modeling for Harmattan Season in Zaria, Nigeria },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 10 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 39-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number10/13287-0891/ },
doi = { 10.5120/13287-0891 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:45:35.654536+05:30
%A Akinsanmi O.
%A Ekundayo K. R.
%T Neural Network based Electrostatic Fields Distribution Modeling for Harmattan Season in Zaria, Nigeria
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 10
%P 39-45
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents the application of neural network to the electrostatic field distribution modeling using harmattan season data in Zaria, Nigeria. The data was captured through an on-line mechanism for twenty-four months by the computer using the Microsoft Office Excel Program for twenty-four months (February, 2007 - February, 2009). The focus of the analysis is determining the effect of environmental factors such as temperature, pressure and relative humidity on the static electric field during the harmattan season. The plots of the electrostatic field against the variation of the environmental factors were used as the qualitative analytical tools and yielded a non-linear relationship. The data was analyzed using Neural Network version 3. 24 Software, to establish predictive models for Harmattan outside and inside Scenarios. The result of the analyses yielded good neural statistical values of Root Mean Square Error (RMSE) of 0. 09, and Pearson R value of 0. 76 for outside Scenario. Similarly for Harmattan inside Scenario, gives a RMSE value of 0. 14, and Pearson R value of 0. 77 respectively, which are reflections of a good model. The result was further buttressed by the plot of the Neural Network based Electrostatic Fields distribution modeling of the experimental and the predicted parameters. With the insignificant values of the RMSE, Pearson R value which are reflections of the closeness of the predicted and the experimental parameters, hence the could be relied upon to predict the electrostatic fields during harmattan in Zaria, Nigeria.

References
  1. S. Arisariwong, and S. Cahroenseang, "Reducing steady state error of a direct drive robot using Neuro-Fuzzy control", Proceedings of Second Asian Symposium on Industrial Automation and Robotics, Bangkok, Thailand, 2001.
  2. B. Nath, "Evolutionary and neural computation," bnath@csse. unimelb. edu. au, The University of Melbourne, Computer Science and Software Engineering, Semester 2, 2007, pp. 433-679.
  3. F. Qiu, "Neuro-Fuzzy based analysis of hyper spectral Imagery", Photogrammetric Engineering & Remote Sensing, vol. 74, No. 10, pp. 1235–1247, October 2008.
  4. R. Fuller, "Neuro-Fuzzy methods for modeling and fault diagnosis," E"otv"os Lor'and university, Budapest Vacation School, Lisbon (Aug. 31 and September 1, 2002), pp. 1-22, 2001.
  5. D. Ihe, "Use of artificial neural network and fuzzy logic for Integrated Water Management: Review of Applications", Project Report, 2000, pp. 3-5.
  6. A. Abraham, and N. S. Philip, "Soft computing models for weather forecasting", 2001, http://meghnad. iucaa. ernet. in/~nspp/scs. pdf.
  7. A. Rotshtein, M. Posner, and H. Rakytyanska, "Prediction of the results of football games based on fuzzy model with genetic and neuro tuning", Eastern European Journal of Enterprise Technologies, 2003, pp. 10 – 15.
  8. V. Edwards, "Artificial neural networks", 1998, http://aspac. web. unsw. edu. au/Media%20Room/week4
  9. Purvis, M. , Kaasabov, N. , Benwell, G. , Zhou, G. , and Zhang, F. , "Neuro-Fuzzy methods for environmental modeling", J. Sys. Res. Info. Sys. , vol. 8, pp. 221 – 239, 1999.
  10. M. B. Mu'azu, "Forecasting and modeling statistical phenomena using neurofuzzy logic: A case study of rainfall forecasting for Zaria", PhD Dissertation, Dept. Elect. Eng. ,Ahmadu Bello Univ. , Zaria, 2006.
  11. M. W. Gardner, and S. R. Dorling, "Artificial Neural Network (Multilayer Perceptron)- a review of applications in atmospheric sciences", Atmospheric Environment, vol. 32, 1998, pp. 2627-2636.
  12. I. Maqsood, R. K. Muhammad, and A. Abraham, Neurocomputing Based Canadian Weather Analysis. Computational Intelligence and Applications, Dynamic Publishers Inc. , USA, 2002, pp. 39-44.
  13. S. Chattopadhyay, Soft computing techniques in combating the complexity of the atmosphere – A review, 2008, http://arxiv. org/ftp/nlin/papers/0608/0608052. pdf
  14. O. Akinsanmi, B. G. Bajoga, and D. D. Dajab, "Comparative analysis of electric field measurement in Zaria". Adv. Mater. Res. , vol. 62-64, 2009, pp. 141-146. Neural Ware, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Electrostatic field Neural network Electrostatic fields Distribution models environmental factors