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Reseach Article

Air Temperature Prediction using Artificial Neural Network for Anyigba, North-Central Nigeria

by Akolo J. A. Felix A. S. Okoh D.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 10
Year of Publication: 2019
Authors: Akolo J. A. Felix A. S. Okoh D.
10.5120/ijca2019919506

Akolo J. A. Felix A. S. Okoh D. . Air Temperature Prediction using Artificial Neural Network for Anyigba, North-Central Nigeria. International Journal of Computer Applications. 177, 10 ( Oct 2019), 29-34. DOI=10.5120/ijca2019919506

@article{ 10.5120/ijca2019919506,
author = { Akolo J. A. Felix A. S. Okoh D. },
title = { Air Temperature Prediction using Artificial Neural Network for Anyigba, North-Central Nigeria },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2019 },
volume = { 177 },
number = { 10 },
month = { Oct },
year = { 2019 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number10/30935-2019919506/ },
doi = { 10.5120/ijca2019919506 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:45:30.864624+05:30
%A Akolo J. A. Felix A. S. Okoh D.
%T Air Temperature Prediction using Artificial Neural Network for Anyigba, North-Central Nigeria
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 10
%P 29-34
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Temperature prediction is the application of science and technology to predict the state of the temperature for a future time and a given location. Temperature prediction is important to protect life and property. Temperature prediction is made by collecting quantitative data about the current state of the atmosphere. In this paper, an artificial neural network was employed using the Levenberg-Marquardt backpropagation to develop the prediction model. The Root Mean Square Error is then calculated between the perceptron and the desired output for the input vectors. This back-propagation approach is chosen for this training because, from the works of literature reviewed, it is regarded as one of the most efficient training algorithms because of its fast and stable convergence and suitable for training small and medium-sized problems in the artificial neural-networks field. Our study used four years’ data (2013-2016) gotten from Atmospheric Monitoring Equipment Network Automatic weather station situated at Centre for Atmospheric Research, Anyigba North Central Nigeria. The proposed model is tested using the network of the hidden neuron with the least root mean square error to the observed data. The outcome of the predicted values is compared with the observed values. After comparing the results with the observed values, it shows that our model has the potential for temperature prediction.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Network Backpropagation Multi-layer perceptron Air Temperature Prediction.