Risk analysis and prediction of out-of-seam dilution in longwall mining

Autor:	Golam Reza Saeedi, Ali Bahri Najafi, Mohammad Ali Ebrahimi Farsangi
Rok vydání:	2014
Předmět:	Engineering Coefficient of determination Rock engineering Artificial neural network Mean squared error Risk analysis (engineering) business.industry Risk analysis (business) Longwall mining Geotechnical Engineering and Engineering Geology business Dilution
Zdroj:	International Journal of Rock Mechanics and Mining Sciences. 70:115-122
ISSN:	1365-1609
DOI:	10.1016/j.ijrmms.2014.04.015
Popis:	A rock engineering systems (RES) based model is defined to predict the level of risk due to out-of-seam dilution (OSD) in longwall faces. Also, based on the level of risks obtained, a predictive model for the OSD is proposed. Furthermore, an artificial neural networks (ANN) model is developed to predict the OSD. The data collected from thirty-five longwall faces are used to carry out the risk analysis and to develop the predictive models. The results obtained show that the level of risk achieved for each face is in consistence with the corresponding OSD calculated. Also, coefficient of determination ( R 2 ) and root mean square error (RMSE) for the ANN model ( R 2 =0.98, RMSE=1.24) and the RES-based model ( R 2 =0.86, RMSE=3.74) have been obtained. These show the good performances of both models. However, the ANN model has a better performance than the RES-based model.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::38af0e60980e0a483daff111261b5a9d https://doi.org/10.1016/j.ijrmms.2014.04.015 Zobrazit plný text záznamu Full Text from ScienceDirect