Advancing the Prediction and Evaluation of Blast-Induced Ground Vibration Using Deep Ensemble Learning with Uncertainty Assessment
Ground vibration is one of the most dangerous environmental problems associated with blasting operations in mining. Therefore, accurate prediction and controlling the blast-induced ground vibration are imperative for environmental protection and sustainable development. The empirical approaches give...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Geosciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3263/15/5/182 |
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| Summary: | Ground vibration is one of the most dangerous environmental problems associated with blasting operations in mining. Therefore, accurate prediction and controlling the blast-induced ground vibration are imperative for environmental protection and sustainable development. The empirical approaches give inaccurate results, as evident in the literature. Hence, numerous researchers have started to use fast-growing soft computing approaches that are satisfying in prediction performance. However, achieving high-prediction performance and detecting prediction uncertainty is crucial, especially in blasting operations. This study aims to propose a deep ensemble model to predict the blast-induced ground vibration and quantify the prediction uncertainty, which is usually not addressed. This study used 200 published data from ten granite quarry sites in Ibadan and Abeokuta areas, Nigeria. The empirical equation (United States Bureau of Mines-based approach) was applied for comparison. The comparison of the models demonstrated that the proposed deep ensemble model achieved superior performance, offering more accurate predictions and more reliable uncertainty quantification. Specifically, it exhibited the lowest root mean square error (22.674), negative log-likelihood (4.44), and mean prediction interval width (1.769), alongside the highest <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn><mo> </mo></mrow></msup></mrow></semantics></math></inline-formula>value (0.77) and prediction interval coverage probability (0.95). The deep ensemble model reached the desired coverage of 95%, demonstrating that uncertainty was not underestimated or overestimated. |
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| ISSN: | 2076-3263 |