College of Mining, Liaoning Technical University, Fuxin, Liaoning, China.
School of Civil Engineering, Wuhan Univerisity, Wuhan, Hubei, China.
PLoS One. 2023 Apr 26;18(4):e0284815. doi: 10.1371/journal.pone.0284815. eCollection 2023.
The problem of dust pollution in the open-pit coal mine significantly impacts the health of staff, the regular operation of mining work, and the surrounding environment. At the same time, the open-pit road is the largest dust source. Therefore, it analyzes the influencing factors of road dust concentration in the open-pit coal mine. It is of practical significance to establish a prediction model for scientific and effective prediction of road dust concentration in the open pit coal mine. The prediction model helps reduce dust hazards. This paper uses the hourly air quality and meteorological data of an open-pit coal mine in Tongliao City, Inner Mongolia Autonomous Region, from January 1, 2020, to December 31, 2021. Create a CNN-BiLSTM-Attention multivariate hybrid model consisting of a Convolutional Neural Network (CNN), a bidirectional long short-term memory neural network (BiLSTM), and an attention mechanism, Prediction of PM2.5 concentration in the next 24h. Establish prediction models of parallel and serial structures, and carry out many experiments according to the change period of the data to determine the optimal configuration and the input and output size. Then, a comparison of the proposed model and Lasso regression, SVR, XGBoost, LSTM, BiLSTM, CNN-LSTM, and CNN-BiLSTM models for short-term prediction (24h) and long-term prediction (48h, 72h, 96h, and 120h). The results show that the CNN-BiLSTM-Attention multivariate mixed model proposed in this paper has the best prediction performance. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) of the short-term forecast (24h) are 6.957, 8.985, and 0.914, respectively. Evaluation indicators of long-term forecasts (48h, 72h, 96h, and 120h) are also superior to contrast models. Finally, we used field-measured data to verify, and the obtained evaluation indexes MAE, RMSE, and R2 are 3.127, 3.989, and 0.951, respectively. The model-fitting effect was good.
露天煤矿的粉尘污染问题严重影响工作人员的健康、矿山开采作业的正常进行以及周边环境。同时，露天道路是最大的粉尘源。因此，分析露天煤矿道路粉尘浓度的影响因素，建立科学有效的露天煤矿道路粉尘浓度预测模型，对降低粉尘危害具有重要的现实意义。本文利用内蒙古自治区通辽市某露天煤矿 2020 年 1 月 1 日至 2021 年 12 月 31 日的逐时空气质量和气象数据，构建了由卷积神经网络（CNN）、双向长短时记忆神经网络（BiLSTM）和注意力机制组成的 CNN-BiLSTM-Attention 多元混合模型，对未来 24h 的 PM2.5 浓度进行预测。建立了并行和串行结构的预测模型，并根据数据的变化周期进行了多次实验，确定了最优的配置和输入输出大小。然后，将所提出的模型与 Lasso 回归、SVR、XGBoost、LSTM、BiLSTM、CNN-LSTM 和 CNN-BiLSTM 模型进行了比较，用于短期预测（24h）和长期预测（48h、72h、96h 和 120h）。结果表明，本文提出的 CNN-BiLSTM-Attention 多元混合模型具有最佳的预测性能。短期预测（24h）的平均绝对误差（MAE）、均方根误差（RMSE）和决定系数（R2）分别为 6.957、8.985 和 0.914，长期预测（48h、72h、96h 和 120h）的评价指标也优于对比模型。最后，我们使用现场实测数据进行了验证，得到的评价指标 MAE、RMSE 和 R2 分别为 3.127、3.989 和 0.951，模型拟合效果良好。
