AN IMPROVED RAINFALL PREDICTION MODEL FOR MINIMIZING THE NEGATIVE IMPACT OF INCREASED RAINFALL DAYS IN MINNA, NIGERIA USING ARTIFICIAL NEURAL NETWORK.
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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
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References
Abdulkadir, T. S., Salami, A. W., & Kareem, A. G. (2012). Artificial Neural Network
Modelling of Rainfall in Ilorin, Kwara State, Nigeria. Journal of Research Information in Civil Engineering.
Abisoye, O.A. & Jimoh, R.G. (2018). Comparative Study on the Prediction of Symptomatic and Climatic based Malaria Parasite Counts Using Machine Learning Models", International Journal of Modern Education and Computer Science (IJMECS), Vol.10, No.4, pp. 18-25, 2018.DOI: 10.5815/ijmecs.2018.04.03
Aftab, S. and Ahmad, M. (2018). Rainfall Prediction using Data Mining Techniques: A
Systematic Literature Review (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 5.
Brownlee, J. (2020). Four Types of Classification tasks in Machine learning.
Received from: www.machinelearningmastery.com
Bukka, U. A., Muhammad, B., & Yahaya, T. I. (2017). Effect of flooding on livelihood of
Communities in Muwo District, Mokwa Local Government Area, Niger State, Nigeria. International Journal of Scientific & Engineering Research Volume 8, 1615 ISSN 2229-5518
Fokas, A., Grigoryan, A., Kubble, T., Zeggarhnski, B. (2018). Mathematical Physics. Imperial College Press. P 5-6.
Geo Dept. FUT Minna (2021). Department of Geography, Federal University of Technology, Minna.
Halliday, D., Sesnick, R. and Walker, J. (2006). Fundamentals of Physics. 5th Edition. Wiley, India. P 456.
Jerzy, L. (2006). Computational Chemistry. Volume 10, World Scientific Publishing. p 121
Julie M. D., and Kannan B., (2010). Significance of Classification Techniques in Prediction of Learning Disabilities. Cochin- 683 107, India.
Kumar, S. (2020). Data Splitting Technique to fit any Machine Learning Model. Received from: www.towardsdatascience.com
Larose, D. T. (2005). Discovering Knowledge in Data: An Introduction to Data Mining. Copyright C John Wiley & Sons, Inc. ISBN 0-471-66657-2
Pepin, N. C. (2017). A comparison of simultaneous temperature and humidity observations from the SW and NE slopes of Kilimanjaro: The role of slope aspect and differential land-cover in controlling mountain climate. Global and Planetary Change. Vol. 157, pp:244-258,
Umar, A. T. (2012). Analysis of Extreme Rainfall Events and Risk of Drought and Flood occurrences in Nigeria. Nigeria Geographical Journal, Volume 8(2).
Theerthagiri, P., Jeena, J., Usha, A, R., & Vamsidhar., (2020). Prediction of COVID-19
Possibilities using KNN Classification Algorithm. Received from: www. Researchsquare.com/articl/ rs-70985/v2 doi:10.21203/rs.3.rs-70985/v2
Tyagi, N. and Kumar, A. (2017). "Comparative Analysis of Backpropagation and RBF Neural Network on Monthly Rainfall Prediction, International. Conference Invention Computer Technology. ICICT vol. 1