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Research on the air quality prediction model of Wuhai mining area based on deep learning

机译：基于深度学习的乌海矿区空气质量预测模型研究

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With the large-scale and high-intensity mining of coal resources in the Wuhai mining area, the destruction of soil and erosion of rocks has intensified, causing a large amount of surface soil spalling from the mine body and serious damage to the surface vegetation, which has had a serious impact on the quality of the environment in and around the mine. This paper focuses on the corresponding early warning research on air quality in the mining area of Wuhai, and constructs Deep Recurrent Neural Network (DRNN) and Deep Long Short Time Memory Neural Network (DLSTM) air quality prediction models based on the filtered weather factors. The simulation results are also compared and find that the prediction results of DLSTM are better than those of DRNN, with a prediction accuracy of 92.85%. The model is able to accurately predict the values and trends of various air pollutant concentrations in the mining area of Wuhai.
机译：随着乌海矿区煤炭资源的大规模和高强度挖掘，土壤和岩石侵蚀的破坏增长，导致大量的表面土壤从矿体身体剥落，对表面植被严重损坏， 这对矿井和周围环境的质量产生了严重影响。 本文重点介绍了乌海矿区空气质量的相应预警研究，基于过滤的天气因子构建了深度复发性神经网络（DRNN）和深度长的短时间内存神经网络（DLSTM）空气质量预测模型。 还比较仿真结果，发现DLSTM的预测结果优于DRNN的预测结果，预测精度为92.85％。 该模型能够准确地预测武城矿区各种空气污染物浓度的价值观和趋势。

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