Prediction of Road Subsidence Caused by Underground Mining Activities by Artificial Neural Networks

• Autorzy: Nguyen Hung Viet , Le Duyen Quang , Nguyen Long Quoc , Lipecki Tomasz

Identyfikatory

DOI

10.29227/IM-2023-02-49

Warianty tytułu

PL

Przewidywanie osiadania dróg spowodowanego podziemną działalnością górniczą za pomocą sztucznych sieci neuronowych

• Konferencja: POL-VIET 2023 — the 7th International Conference POL-VIET
• Języki publikacji: EN

Abstrakty

EN

Mining-induced road subsidence is a significant concern in areas with extensive underground mining activities. Therefore, the prediction of road subsidence is crucial for effective land management and infrastructure planning. This paper applies an artificial neural network (ANN) to predict road subsidence caused by underground mining activities in Vietnam. The ANN model proposed in this study is adopted relying on the recursive multistep prediction process, in which the predicted value in the previous step is appended to the time series to predict the next value. The entire dataset of 12 measured epochs covering 12 months with a 1-month repeat time is divided into the training set by the first 9 measured epochs and the test set by the last 3 measured epochs. K-fold cross validation is first applied to the training set to determine the best model’s hyperparameters, which are then adopted to predict land subsidence of the test set. Absolute errors of the predicted road subsidence depend on the separated time between the last measured epoch and the predicted epoch. Those errors at the 10th month of the three tested points are 3.0%, 0.1 %, and 0.1%, which increase to 4.8%, 3.3%, and 1.5% at the 11th month, and 7.2%, 2.5% and 1.3% at the 12th month. The absolute errors are found to be small, which were all ranged with 0.5 mm and demonstrates that the proposed method utilizing ANN in this study can produce good prediction for road subsidence time series at mining areas.

Słowa kluczowe

EN

subsidence prediction; underground mine; machine learning; ANN

PL

górnictwo podziemne; ANN; sieci neuronowe

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2023
• Tom: no. 2, vol. 1
• Strony: 335--340
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Nguyen Hung Viet

• Faculty of Information Technology, University of Transport and Communications, Hanoi, Vietnam

• https://orcid.org/0009-0005-1104-5161

autor

Le Duyen Quang

• Faculty of Petroleum and Energy, Hanoi University of Mining and Geology, Hanoi, Vietnam

autor

Nguyen Long Quoc

• Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Hanoi, Vietnam
• Innovations for Sustainable and Responsible Mining (ISRM) Research Group, Hanoi University of Mining and Geology, Hanoi, Vietnam

• https://orcid.org/0000-0002-4792-3684

autor

Lipecki Tomasz

• Faculty of Geo-data Science, Geodesy and Environmental Engineering, AGH University of Krakow, Kraków, Poland;

• https://orcid.org/0000-0002-3204-7343

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu „Społeczna odpowiedzialność nauki” - moduł: Popularyzacja nauki i promocja sportu (2022-2023)