A comparative study of empirical and ensemble machine learning algorithms in predicting air over pressure in open pit coal mine

Nguyen, Hoang; Bui, Xuan-Nam; Tran, Quang-Hieu; Pham, Van Hoa; Nguyen, Dinh-An; Le, Thi Thu Hoa; Le, Qui-Thao; Do, Ngoc-Hoan; Bao, Tran Dinh; Bui, Hoang-Bac; Moayedi, Hossein

doi:10.1007/s11600-019-00396-x

Artykuł - szczegóły

Tytuł artykułu

A comparative study of empirical and ensemble machine learning algorithms in predicting air over pressure in open pit coal mine

Autorzy

Nguyen Hoang , Bui Xuan-Nam , Tran Quang-Hieu , Pham Van Hoa , Nguyen Dinh-An , Le Thi Thu Hoa , Le Qui-Thao , Do Ngoc-Hoan , Bao Tran Dinh , Bui Hoang-Bac , Moayedi Hossein

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-019-00396-x

Warianty tytułu

Języki publikacji

Abstrakty

This study aims to take into account the feasibility of three ensemble machine learning algorithms for predicting blast-induced air over-pressure (AOp) in open-pit mine, including gradient boosting machine (GBM), random forest (RF), and Cubist. An empirical technique was also applied to predict AOp and compared with those of the ensemble models. To employ this study, 146 events of blast were investigated with 80% of the total database (approximately 118 blasting events) being used for developing the models, whereas the rest (20%~28 blasts) were used to validate the models’ accuracy. RMSE, MAE, and R² were used as performance indices for evaluating the reliability of the models. The fndings revealed that the ensemble models yielded more precise accuracy than those of the empirical model. Of the ensemble models, the Cubist model provided better performance than those of RF and GBM models with RMSE, MAE, and R² of 2.483, 0.976, and 0.956, respectively, whereas the RF and GBM models provided poorer accuracy with an RMSE of 2.579, 2.721; R² of 0.953, 0.950, and MAE of 1.103, 1.498, respectively. In contrast, the empirical model was interpreted as the poorest model with an RMSE of 4.448, R² of 0.872, and MAE of 3.719. In addition, other fndings indicated that explosive charge capacity, spacing, stemming, monitoring distance, and air humidity were the most important inputs for the AOp predictive models using artifcial intelligence.

Słowa kluczowe

open-pit mine random forest gradient boosting air over-pressure ensemble algorithm cubist

kopalnia odkrywkowa las losowy zwiększanie gradientu

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2020

Tom

Vol. 68, no. 2

Strony

325--336

Opis fizyczny

Bibliogr. 63 poz.

Twórcy

autor

Nguyen Hoang

Department of Surface Mining, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam
Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam

autor

Bui Xuan-Nam

buixuannam@humg.edu.vn

Department of Surface Mining, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam
Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam

autor

Tran Quang-Hieu

Department of Surface Mining, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam

autor

Pham Van Hoa

Department of Surface Mining, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam

autor

Nguyen Dinh-An

Department of Surface Mining, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam

autor

Le Thi Thu Hoa

Department of Surface Mining, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam

autor

Le Qui-Thao

Department of Surface Mining, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam
Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam

autor

Do Ngoc-Hoan

Department of Surface Mining, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam
Faculty of Mining, Saint-Petersburg Mining University, Saint Petersburg, Russia

autor

Bao Tran Dinh

Department of Surface Mining, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam

autor

Bui Hoang-Bac

Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam
Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam

autor

Moayedi Hossein

Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)

Korekta artykułu znajduje się w Vol. 69, no. 5/2021, na stronach 1609-1610. Numer DOI korekty: 10.1007/s11600-019-00396-x

Typ dokumentu

Bibliografia

Identyfikator YADDA

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