Natural Resources Research Publications on Resourcing Future Generations, and Introduction of Papers in this Special Issue

Carranza, Emmanuel John M.

doi:10.1007/s11053-018-9369-4

Cited by 4 publications

(3 citation statements)

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“…In recent years, artificial intelligence (AI) has become more popular and widely applied in many different fields. Review of literature showed that AI had been involved in many aspects such as mineralizing geochemical anomalies [33][34][35], optimizing operational mine planning [36], civil engineering [37,38], analyzing mineral systems [39], mineral potential mapping [40,41], resourcing future generations [42,43], predicting blast-induced problems [44][45][46][47]. In predicting blast-induced PPV, the feasibility of a support vector machine (SVM) algorithm was studied and applied by Hasanipanah et al [7] to predict blast-induced PPV in Bakhtiari Dam, Iran.…”

Section: Introductionmentioning

confidence: 99%

Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam

Nguyen

2019

SN Appl. Sci.

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Blast-induced ground vibration (PPV) is one of the effects of the hazard in open-pit mines. It can make vibration of the structure, instability of slope and bench, impact on underground water, and the surrounding residential. Therefore, the precise prediction of blast-induced PPV is needed to minimize undesirable effects on the surrounding environment. Also, one of the most important objectives of this study is to determine the site characteristics for the application of controlled blasting techniques. In this study, the support vector regression (SVR) approach was considered and developed for predicting blast-induced PPV in an open-pit coal mine (Vietnam) as a case study. Three forms of the equation include linear (L), polynomial (P), and radius basis function (RBF) were applied to develop the SVR models. For comparison purpose, an empirical technique was also referred to as estimate blast-induced PPV in this study on the same training dataset. A database with 181 blasting events was used for this aim. Performance indicators such as root-mean-square error (RMSE) and the coefficient of determination (R 2 ) were used to compare and evaluate the performance of the predictive models. The results showed that SVR was an effective approach for predicting blast-induced PPV in this study. Among three forms of the equation, the SVR model with RBF was the most superior model for predicting blast-induced PPV in this study with an RMSE of 0.396, R 2 of 0.924, and MAE of 0.135, whereas the empirical model only obtained performance with an RMSE of 0.856, R 2 of 0.643, and MAE of 0.575. This study provided an overview of the SVR approach in predicting blast-induced PPV. A comparison of different kernel functions for the selection of the SVR model is needed to find out the best model for predicting blast-induced PPV.

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Section: Introductionmentioning

confidence: 99%

Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam

Nguyen

2019

SN Appl. Sci.

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“…NRR is recognized customarily for papers on methodologies for estimation/assessment of mineral resources and modeling/mapping of mineral potential or prospectivity (Carranza, 2017(Carranza, , 2018. Indeed, the NRR articles that contributed significantly to its first three IF were mostly (if not all) concerned with mineral resources (Carranza, 2019(Carranza, , 2020.…”

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confidence: 99%

NRR Turns 30, with a Blast: An Impact Factor of 5.146!

Carranza

2021

Nat Resour Res

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“…Our journal Natural Resources Research (NRR) is known traditionally for articles on the development of methods for assessment/estimation of and mapping of potential (or prospectivity) for mineral resources (Carranza 2017(Carranza , 2018b. In fact, the top 10 most-cited NRR articles published in 2015 and 2016 that contributed significantly to our jour-nalÕs first impact factor (IF), an astounding 3.094, were all concerned with mineral resources (Carranza 2019).…”

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confidence: 99%