Estimating Air Over-pressure Resulting from Blasting in Quarries Based on a Novel Ensemble Model (GLMNETs–MLPNN)

Nguyen, Hoang; Bui, Xuan‐Nam; Tran, Quang-Hieu

doi:10.1007/s11053-021-09822-8

Cited by 11 publications

(2 citation statements)

References 57 publications

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“…The southeastern coast of China, specifically, the shift from traditional statistical models to data-driven models fundamentally directly affects the predictive performance of susceptibility modeling [37]. The widely used machine learning models currently include decision trees (DT) [38][39][40], multi-layer perceptron network (MLPNN) [41,42], support vector machine (SVM) [43][44][45], etc. Furthermore, some scholars have attempted to use ensemble models to enhance and improve the prediction errors of single classifiers.…”

Section: Introductionmentioning

confidence: 99%

Tempo-Spatial Landslide Susceptibility Assessment from the Perspective of Human Engineering Activity

Zeng,

Guo,

Wang

et al. 2023

Remote Sensing

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The expansion of mountainous urban areas and road networks can influence the terrain, vegetation, and material characteristics, thereby altering the susceptibility of landslides. Understanding the relationship between human engineering activities and landslide occurrence is of great significance for both landslide prevention and land resource management. In this study, an analysis was conducted on the landslide caused by Typhoon Megi in 2016. A representative mountainous area along the eastern coast of China—characterized by urban development, deforestation, and severe road expansion—was used to analyze the spatial distribution of landslides. For this purpose, high-precision Planet optical remote sensing images were used to obtain the landslide inventory related to the Typhoon Megi event. The main innovative features are as follows: (i) the newly developed patch generating land-use simulation (PLUS) model simulated and analyzed the driving factors of land-use land-cover (LULC) from 2010 to 2060; (ii) the innovative stacking strategy combined three strong ensemble models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—to calculate the distribution of landslide susceptibility; and (iii) distance from road and LULC maps were used as short-term and long-term dynamic factors to examine the impact of human engineering activities on landslide susceptibility. The results show that the maximum expansion area of built-up land from 2010 to 2020 was 13.433 km2, mainly expanding forest land and cropland land, with areas of 8.28 km2 and 5.99 km2, respectively. The predicted LULC map for 2060 shows a growth of 45.88 km2 in the built-up land, mainly distributed around government residences in areas with relatively flat terrain and frequent socio-economic activities. The factor contribution shows that distance from road has a higher impact than LULC. The Stacking RF-XGB-LGBM model obtained the optimal AUC value of 0.915 in the landslide susceptibility analysis in 2016. Furthermore, future road network and urban expansion have intensified the probability of landslides occurring in urban areas in 2015. To our knowledge, this is the first application of the PLUS and Stacking RF-XGB-LGBM models in landslide susceptibility analysis in international literature. The research results can serve as a foundation for developing land management guidelines to reduce the risk of landslide failures.

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Section: Introductionmentioning

confidence: 99%

Tempo-Spatial Landslide Susceptibility Assessment from the Perspective of Human Engineering Activity

Zeng,

Guo,

Wang

et al. 2023

Remote Sensing

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“…In addition to these techniques, ensemble models based on the bagging or/and boosting strategies were also suggested as a potential approach for predicting environmental issues in mine blasting with improved accuracy. Nonetheless, they just have been applied for predicting air over-pressure and flyrock in mine blasting [42][43][44]. In this study, the bagging technique (BA) with four novel ensemble models based on this technique and various machine learning algorithms, including extra trees (ExTree), Support Vector Regression (SVR), K-nearest neighbors (KNN), and decision tree regression (DTR), were applied and combined to predict PPV in open-pit mines, abbreviated as BA-ExTree, BA-SVR, BA-KNN, and BA-DTR.…”

Section: Introductionmentioning

confidence: 99%

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

Tran¹,

Nguyen²,

Bui³

2023

Computer Modeling in Engineering &Amp; Sciences

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This study considered and predicted blast-induced ground vibration (PPV) in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms. Accordingly, four machine learning algorithms, including support vector regression (SVR), extra trees (ExTree), K-nearest neighbors (KNN), and decision tree regression (DTR), were used as the base models for the purposes of combination and PPV initial prediction. The bagging regressor (BA) was then applied to combine these base models with the efforts of variance reduction, overfitting elimination, and generating more robust predictive models, abbreviated as BA-ExTree, BA-KNN, BA-SVR, and BA-DTR. It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before, and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model, as well. In addition, two empirical models (i.e., USBM and Ambraseys) were also treated and compared with the bagging models to gain a comprehensive assessment. With this aim, we collected 300 blasting events with different parameters at the Sin Quyen copper mine (Vietnam), and the produced PPV values were also measured. They were then compiled as the dataset to develop the PPV predictive models. The results revealed that the bagging models provided better performance than the empirical models, except for the BA-DTR model. Of those, the BA-ExTree is the best model with the highest accuracy (i.e., 88.8%). Whereas, the empirical models only provided the accuracy from 73.6%-76%. The details of comparisons and assessments were also presented in this study.

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Application of an expert extreme gradient boosting model to predict blast-induced air-overpressure in quarry mines

He,

Armaghani,

Lai

et al. 2024

Applications of Artificial Intelligence in Mining, Geotechnical and Geoengineering

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Estimating Air Over-pressure Resulting from Blasting in Quarries Based on a Novel Ensemble Model (GLMNETs–MLPNN)

Cited by 11 publications

References 57 publications

Tempo-Spatial Landslide Susceptibility Assessment from the Perspective of Human Engineering Activity

Tempo-Spatial Landslide Susceptibility Assessment from the Perspective of Human Engineering Activity

Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

Application of an expert extreme gradient boosting model to predict blast-induced air-overpressure in quarry mines

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