What question did this study set out to answer?

The aim is to enhance the accuracy of peak particle velocity predictions in open-pit mining operations using machine learning and optimization techniques.

February 2, 2026Open Access

Hybrid Machine Learning with Metaheuristic Optimization for Predicting Peak Particle Velocity in Open-Pit Mines

Key Points

The aim is to enhance the accuracy of peak particle velocity predictions in open-pit mining operations using machine learning and optimization techniques.
Integrated machine learning framework combining random forest and metaheuristic optimization algorithms
Utilized whale optimization algorithm, particle swarm optimization, and grey wolf optimizer
Analyzed a dataset of 175 blasting events with 11 geomechanical and blast design parameters
Compared performance against conventional machine learning methods and empirical formulas
Developed an interactive graphical user interface for real-time predictions and dynamic blast design optimization.
WOA-RF model achieved the highest predictive accuracy and robustness among the optimized models
Demonstrated strong generalization capabilities compared to baseline methods
Distance was identified as the primary factor affecting peak particle velocity
Provided insights into the optimization efficiency of the three metaheuristic algorithms in a unified model.

Abstract

Abstract Ground vibrations induced by blasting pose significant environmental and structural challenges in opencast mining operations. Accurate prediction of peak particle velocity (PPV) is crucial for mitigating potential structural damage and ensuring operational safety. This study proposes an integrated machine learning framework that combines a random forest (RF) model with three metaheuristic optimization algorithms, whale optimization algorithm (WOA), particle swarm optimization (PSO), and grey wolf optimizer (GWO), to improve PPV prediction accuracy and robustness. Using a comprehensive dataset of 175 blasting events from the Jayant opencast coal mine in India, incorporating 11 key geomechanical, blast design, and monitoring parameters, the study constructs and compares three hybrid RF models optimized by WOA, PSO, and GWO. The performance of these hybrid models is benchmarked against baseline machine learning methods (support vector regression, kernel extreme learning machine, decision tree) and eight empirical formulas using multiple evaluation metrics. Results demonstrate that the WOA-RF model consistently outperforms others, achieving the highest predictive accuracy with strong generalization capabilities. Furthermore, Shapley additive explanation–based sensitivity analysis elucidates the dominant influence of distance on PPV, validating the physical basis of the models. A key innovation of this work lies in the side-by-side comparative assessment of three metaheuristic algorithms within a unified RF framework, providing valuable insights into their optimization efficiency and model robustness. Complementing the modeling advances, an interactive graphical user interface (GUI) was developed to facilitate practical adoption, enabling rapid local data learning, real-time PPV prediction, and dynamic blast design optimization. This GUI enhances engineer autonomy and supports informed decision-making in field applications. The proposed hybrid framework and its user-friendly interface offer a significant contribution to advancing predictive modeling and operational control of blasting-induced ground vibrations in mining engineering.

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