A Conceptual Machine Learning Approach for Rainfall Pattern Prediction in Umuahia Metropolis

Eunice Chioma Agina; Uduak David George; Patience Usoro Usip

doi:10.32628/IJSRSET2411444

Authors

Eunice Chioma Agina Department of Computer Science, Michael Okpara University of Agriculture, Umudike, Nigeria Author
Uduak David George Department of Computer Science, University of Uyo, Uyo, Nigeria Author
Patience Usoro Usip Department of Computer Science, University of Uyo, Uyo, Nigeria Author

DOI:

https://doi.org/10.32628/IJSRSET2411444

Keywords:

Rainfall Pattern, Artificial Neural Network, Multilayer Perceptron, Prediction, Machine Learning

Abstract

The erratic nature of rainfall in Umuahia metropolis of Abia State, Nigeria due to the constant variations in atmospheric conditions, results in extreme weather conditions such as drought and flooding which pose dire consequences to human beings and the environment resulting in loss of lives, damage to agricultural produce and vital infrastructure. This study seeks to present a conceptual approach using a machine learning algorithm to support rainfall pattern prediction. A comprehensive reviews of related works was carried out on Artificial Neural Network (ANN), Support Vector Machine (SVM), Multilinear Regression (MLR) and Extreme Gradient Boosting (XGBoost) applications in prediction. The min-max normalization technique was deployed to render the dataset in a common normalized data range. A 4-10-1 architecture of a Multilayer Perceptron (MLP) was designed with four nodes at the input layer, ten nodes at the hidden processing layer, and one node at the output layer for rainfall pattern prediction. Implementation of this study with real data and its comparison with other machine learning algorithms are highly recommended for further study in this domain.

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