# Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

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## Abstract

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^{2}(correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R

^{2}= 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure.

## 1. Introduction

## 2. Dataset Used

## 3. Artificial Neural Network (ANN)

## 4. Combination of Multiple ANN Models

## 5. Performance Indexes for Evaluation of the Models

^{2}(correlation coefficient). They are computed as follow:

## 6. Results

## 7. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**(

**a**) Blast points; (

**b**) ANFO explosive (ammonium nitrate/fuel oil); (

**c**) iGeoTrans app with the monitoring points.

**Figure 4.**Artificial neural network (ANN) architecture for predicting blast-induced fly-rock efficiently.

B (m) | S (m) | ST (m) | W (Kg) | PF (kg/m^{3}) | FR (m) | |
---|---|---|---|---|---|---|

Minimum | 1.272 | 2.010 | 0.5194 | 166.3 | 0.2578 | 101.1 |

Mean | 2.090 | 3.021 | 1.3038 | 1236.4 | 0.4013 | 167.7 |

Maximum | 2.982 | 4.137 | 2.5236 | 2820.5 | 0.5963 | 290.1 |

Model | MAE | MAPE | RMSE | VAF | R^{2} | Rank for MAE | Rank for MAPE | Rank for RMSE | Rank for VAF | Rank for R^{2} |
---|---|---|---|---|---|---|---|---|---|---|

ANN 5-7-5-1 | 3.301 | 0.021 | 6.231 | 97.563 | 0.976 | 6 | 5 | 5 | 5 | 5 |

ANN 5-10-8-1 | 3.416 | 0.022 | 6.413 | 97.419 | 0.974 | 4 | 4 | 2 | 2 | 2 |

ANN 5-14-9-1 | 3.326 | 0.021 | 6.122 | 97.648 | 0.976 | 5 | 5 | 6 | 6 | 5 |

ANN 5-18-13-1 | 3.482 | 0.023 | 6.309 | 97.502 | 0.975 | 3 | 3 | 4 | 4 | 3 |

ANN 5-21-16-1 | 3.67 | 0.024 | 6.672 | 97.206 | 0.972 | 2 | 1 | 1 | 1 | 1 |

ANN 5-25-21-15-1 | 3.695 | 0.024 | 6.361 | 97.460 | 0.975 | 1 | 1 | 3 | 3 | 3 |

EANNs | 2.908 | 0.019 | 4.954 | 98.464 | 0.985 | 7 | 7 | 7 | 7 | 7 |

**Note:**MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-squared error), VAF (variance accounted for), and R

^{2}(correlation coefficient).

Model | MAE | MAPE | RMSE | VAF | R^{2} | Rank for MAE | Rank for MAPE | Rank for RMSE | Rank for VAF | Rank for R^{2} |
---|---|---|---|---|---|---|---|---|---|---|

ANN 5-7-5-1 | 3.227 | 0.019 | 5.71 | 97.242 | 0.974 | 4 | 5 | 2 | 3 | 2 |

ANN 5-10-8-1 | 3.162 | 0.019 | 5.55 | 97.349 | 0.975 | 6 | 5 | 4 | 4 | 3 |

ANN 5-14-9-1 | 3.202 | 0.02 | 5.684 | 97.225 | 0.975 | 5 | 4 | 3 | 2 | 3 |

ANN 5-18-13-1 | 3.322 | 0.021 | 5.459 | 97.437 | 0.975 | 2 | 2 | 6 | 6 | 3 |

ANN 5-21-16-1 | 3.300 | 0.021 | 5.945 | 96.919 | 0.973 | 3 | 2 | 1 | 1 | 1 |

ANN 5-25-21-15-1 | 3.568 | 0.022 | 5.541 | 97.373 | 0.975 | 1 | 1 | 5 | 5 | 3 |

EANNs | 2.777 | 0.017 | 4.346 | 98.446 | 0.986 | 7 | 7 | 7 | 7 | 7 |

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**MDPI and ACS Style**

Nguyen, H.; Bui, X.-N.; Nguyen-Thoi, T.; Ragam, P.; Moayedi, H. Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model. *Appl. Sci.* **2019**, *9*, 4554.
https://doi.org/10.3390/app9214554

**AMA Style**

Nguyen H, Bui X-N, Nguyen-Thoi T, Ragam P, Moayedi H. Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model. *Applied Sciences*. 2019; 9(21):4554.
https://doi.org/10.3390/app9214554

**Chicago/Turabian Style**

Nguyen, Hoang, Xuan-Nam Bui, Trung Nguyen-Thoi, Prashanth Ragam, and Hossein Moayedi. 2019. "Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model" *Applied Sciences* 9, no. 21: 4554.
https://doi.org/10.3390/app9214554