Prediction accuracy of ordinary least square and artificial neural network (mlpregressor) in prediction of rainfall and ore productions
1 Department of Mining Engineering, School of Infrastructure, Minerals and Manufacturing Engineering, Federal University of Technology, Akure, Ondo State, Nigeria.
2 Segilola Gold Mining Resources. Ilesha, Osun State, Nigeria.
3 Zhong Sing Mining Investment, Auchi, Edo State, Nigeria.
Research Article
Open Access Research Journal of Engineering and Technology, 2025, 09(01), 021-028.
Article DOI: 10.53022/oarjet.2025.9.1.0069
Publication history:
Received on 31 May 2025; revised on 05 July 2025; accepted on 08 July 2025
Abstract:
Application of ordinary least square (OLS) and artificial neural network (ANN) in prediction of rainfall and production (ore and waste) for better prediction accuracy and sustainability of mining production is hereby study in this paper. The degree and accuracy of both OLS and ANN was examined using rainfall and production data over period of 5 years in an open pit mine in South Eastern part of Nigeria, Ebonyi State. The OLS model performed best compared with MLPRegressor in predicting the project cumulative total using the rainfall days’ values. The OLS could be used to predict 19% of the variance in the project cumulative total. Using daily handling waste, daily handling ore and rainfall days as input, the OLS model predicted 93% variance in the project cumulative total, while the MLP Regressor performed best as it can be used to predict 99% variance in the Project Cummulative total. Hence, MLP Regressor is a better tool in future prediction when compared with OLS.
Keywords:
OLS; ANN; Prediction; Rainfall; Ore and Waste
Full text article in PDF:
Copyright information:
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0