Open AccessMonitoring and prediction of dust concentration in an open-pit mine using a deep-learning algorithm
Author(s) -
Lin Li,
Ruixin Zhang,
Jiandong Sun,
Qian He,
Lingzhen Kong,
Xin Liu
Publication year - 2021
Publication title -
journal of environmental health science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 45
ISSN - 2052-336X
DOI - 10.1007/s40201-021-00613-0
Subject(s) - autoregressive integrated moving average , open pit mining , environmental science , coal mining , pollution , mining engineering , meteorology , particulates , moving average , algorithm , computer science , time series , coal , statistics , machine learning , engineering , mathematics , waste management , chemistry , geography , ecology , organic chemistry , biology
Dust pollution is currently one of the most serious environmental problems faced by open-pit mines. Compared with underground mining, open-pit mining has many dust sources, and a wide area of influence and complicated changes in meteorological conditions can result in great variations in dust concentration. Therefore, the prediction of dust concentrations in open-pit mines requires research and is of great significance for reducing environmental pollution and personal health hazards.