Exploring the Efficacy of Machine Learning Techniques for Predicting Students’ Academic Performances

Abstract

Rainfall as an environmental feat can change fast and yield significant influence in downstream hydrology known as runoff with a variety of implications such as erosion, water quality, and infrastructures. These, in turn, impact the quality of life, sewage systems, agriculture, and tourism of a nation, to mention a few. It chaotic, complex and dynamic nature has necessitated studies in the quest for future direction of such runoff via prediction models. With little successes in use of knowledge driven models – many studies have now turned to data-driven models. Dataset is retrieved from Metrological Center in Lagos, Nigeria for the period 1999–2019 for the Benin-Owena River Basin. Data is split: 70% for train, and 30% for test. Our study adapts a spatial-temporal profile hidden Markov trained deep neural network. Result yields a sensitivity of 0.9, specificity 0.19, accuracy of 0.74, and improvement rate of classification of 0.12. Other ensembles underperformed when compared to proposed model. The study reveals annual rainfall is an effect of variation cycle. Models will help simulate future floods and provide, lead time warnings in flood management. Keywords: Evidence, Rainfall Runoff, Southern Nigeria, Hybrid Ensemble Machine Learning Approach Nwanze Ashioba, Frances Emordi, Patrick Ejeh, Christopher Odiakaose, Christopher Odeh, Obiageli Attoh, & Maduabuchuku Azaka. (2024): Empirical Evidence for Rainfall Runoff in Southern Nigeria Using a Hybrid Ensemble Machine Learning Approach. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 1. Pp 73-86. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P6