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<p>Predicting flyrock is key to safety and efficiency in open pit mining. In this study, we developed and tested four hybrid models utilizing an adaptive neuro–fuzzy inference system (ANFIS) integrated with metaheuristic optimization techniques: Lévy-enhanced Jaya (ANFIS–LJ), bat algorithm (ANFIS–BA), firefly algorithm (ANFIS–FA) and social spider optimization (ANFIS–SSO). Remarkably, the Lévy technique was applied to enhance the JA algorithm and improve the performance of the ANFIS model for predicting flyrock distance. The models were trained and tested using a dataset from Ta Phoi copper mine with 204 blast events and flyrock distance as the target variable. A drone was used to measure flyrock distance in this study with high resolution to capture the entire flyrock phenomenon of each blast. The k-fold cross-validation technique (with 5 folds) was applied to ensure that AI-based models are not only accurate but also generalize well to new data. It helps in evaluating model performance, tuning hyperparameters, reducing overfitting, and providing a more reliable estimate of how the model will perform in predicting blast-induced flyrock.&#xa0;The models were evaluated using MAE (mean absolute error), RMSE (root mean-squared error) and <i>R</i><sup>2</sup>. The result showed that ANFIS–LJ outperformed the other models with MAE of 1.423, RMSE of 1.895 and <i>R</i><sup>2</sup> of 0.981 on the testing dataset. It was also validated through 13 blasts in practice and achieved a high <i>R</i><sup>2</sup> of 0.988, indicating excellent agreement between predicted and observed flyrock distances. Besides, the low MAE (1.322) and RMSE (1.825) values confirmed the model's precision and reliability in predicting flyrock distances. These results confirmed its potential as a valuable tool for optimizing blast designs, enhancing safety, and reducing environmental impacts in real-world engineering applications. This study showed that combining ANFIS with metaheuristic algorithms, especially Lévy-enhanced Jaya algorithm, can produce accurate flyrock prediction. The result can be used to improve the predictive model in open pit mining and decision making. Future study can focus on refining the models and applying them in different mining environments to improve the accuracy.</p>

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Measuring and Predicting Blast-Induced Flyrock Using Unmanned Aerial Vehicles and Lévy Flight Technique-Based Jaya Optimization Algorithm Integrated with Adaptive Neuro-Fuzzy Inference System

  • 期刊论文

  • 发表日期：

    2025年2月9日

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  • Hoang Nguyen,
  • Tran Dinh Bao,
  • Xuan-Nam Bui,
  • Van-Viet Pham,
  • Dinh-An Nguyen,
  • Ngoc-Hoan Do,
  • Le Thi Thu Hoa,
  • Qui-Thao Le,
  • Tuan-Ngoc Le

摘要

Predicting flyrock is key to safety and efficiency in open pit mining. In this study, we developed and tested four hybrid models utilizing an adaptive neuro–fuzzy inference system (ANFIS) integrated with metaheuristic optimization techniques: Lévy-enhanced Jaya (ANFIS–LJ), bat algorithm (ANFIS–BA), firefly algorithm (ANFIS–FA) and social spider optimization (ANFIS–SSO). Remarkably, the Lévy technique was applied to enhance the JA algorithm and improve the performance of the ANFIS model for predicting flyrock distance. The models were trained and tested using a dataset from Ta Phoi copper mine with 204 blast events and flyrock distance as the target variable. A drone was used to measure flyrock distance in this study with high resolution to capture the entire flyrock phenomenon of each blast. The k-fold cross-validation technique (with 5 folds) was applied to ensure that AI-based models are not only accurate but also generalize well to new data. It helps in evaluating model performance, tuning hyperparameters, reducing overfitting, and providing a more reliable estimate of how the model will perform in predicting blast-induced flyrock. The models were evaluated using MAE (mean absolute error), RMSE (root mean-squared error) and R2. The result showed that ANFIS–LJ outperformed the other models with MAE of 1.423, RMSE of 1.895 and R2 of 0.981 on the testing dataset. It was also validated through 13 blasts in practice and achieved a high R2 of 0.988, indicating excellent agreement between predicted and observed flyrock distances. Besides, the low MAE (1.322) and RMSE (1.825) values confirmed the model's precision and reliability in predicting flyrock distances. These results confirmed its potential as a valuable tool for optimizing blast designs, enhancing safety, and reducing environmental impacts in real-world engineering applications. This study showed that combining ANFIS with metaheuristic algorithms, especially Lévy-enhanced Jaya algorithm, can produce accurate flyrock prediction. The result can be used to improve the predictive model in open pit mining and decision making. Future study can focus on refining the models and applying them in different mining environments to improve the accuracy.