AN IMPROVED RAINFALL PREDICTION MODEL FOR MINIMIZING THE NEGATIVE IMPACT OF INCREASED RAINFALL DAYS IN MINNA, NIGERIA USING ARTIFICIAL NEURAL NETWORK.

Authors

Keywords:

Rainfall prediction Model, Artificial Neural Network (ANN).

Abstract

This research aims at predicting the rainfall of Minna metropolis of Niger State, Nigeria using binary classification method of Artificial Neural Network (ANN). In this approach, four atmospheric parameters comprising those of rainfall, relative humidity, minimum temperature and maximum temperature spanning from January 2010 to December 2019 were acquired from the Geography Department of the Federal University of Technology, Minna. The default threshold classification method of ANN was investigated. The result revealed that: for the default threshold of 0.5, a prediction accuracy of 69%, sensitivity of 63%, specificity of 84% an error value of 1.3% and a total of 66 rainfall days were predicted as against 32 rainfall days in the data set.  The implication of this result is that more rainfall days were anticipated in the metropolis which could lead to flooding in long run. It was recommended that for more accurate rainfall prediction, more robust data be used for network training

Dimensions

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Published

2022-12-30

How to Cite

Ibrahim, A. G., Oyedum, O. D., Abisoye, O. A., Usman, M. N., & Ughanze, J. I. (2022). AN IMPROVED RAINFALL PREDICTION MODEL FOR MINIMIZING THE NEGATIVE IMPACT OF INCREASED RAINFALL DAYS IN MINNA, NIGERIA USING ARTIFICIAL NEURAL NETWORK. Nigerian Journal of Physics, 31(2), 151-159. https://njp.nipngr.org/index.php/njp/article/view/70

Issue

Vol. 31 No. 2 (2022): Nigerian Journal of Physics - Vol. 31 No. 2

Section

Articles
Copyright & Licensing

How to Cite

Ibrahim, A. G., Oyedum, O. D., Abisoye, O. A., Usman, M. N., & Ughanze, J. I. (2022). AN IMPROVED RAINFALL PREDICTION MODEL FOR MINIMIZING THE NEGATIVE IMPACT OF INCREASED RAINFALL DAYS IN MINNA, NIGERIA USING ARTIFICIAL NEURAL NETWORK. Nigerian Journal of Physics, 31(2), 151-159. https://njp.nipngr.org/index.php/njp/article/view/70

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