Machine Learning Algorithms for Data Enrichment: A Promising Solution for Enhancing Accuracy in Predicting Blast-Induced Ground Vibration in Open-Pit Mines

Abstract

The issue of blast-induced ground vibration poses a significant environmental challenge in open-pit mines, necessitating precise prediction
and control measures. While artificial intelligence and machine learning models hold promise in addressing this concern, their accuracy
remains a notable issue due to constrained input variables, dataset size, and potential environmental impact. To mitigate these challenges,
data enrichment emerges as a potential solution to enhance the efficacy of machine learning models, not only in blast-induced ground
vibration prediction but also across various domains within the mining industry. This study explores the viability of utilizing machine
learning for data enrichment, with the objective of generating an augmented dataset that offers enhanced insights based on existing
data points for the prediction of blast-induced ground vibration. Leveraging the support vector machine (SVM), we uncover intrinsic
relationships among input variables and subsequently integrate them as supplementary inputs. The enriched dataset is then harnessed
to construct multiple machine learning models, including k-nearest neighbors (KNN), classification and regression trees (CART), and
random forest (RF), all designed to predict blast-induced ground vibration. Comparative analysis between the enriched models and their
original counterparts, established on the initial dataset, provides a foundation for extracting insights into optimizing the performance of
machine learning models not only in the context of predicting blast-induced ground vibration but also in addressing broader challenges
within the mining industry.