Abstract Gas is one of the most dangerous byproducts of coal in mines. Before gas accidents occur, an abnormally increased gas concentration can be observed. Therefore, a prediction of the gas concentration in coal mines is of great significance to prevent the gas accident and ensure the production safety in the mines. By calculating the Pearson correlation coefficient for the gas concentration of different sensors, the spatial correlation of the gas concentration that is monitored for each mining face is verified. We present multi-step prediction results for gas concentration time series based on the ARMA model, the CHAOS model and the Encoder-Decoder model (single-sensor and multi-sensor) and compare these results. The Encoder-Decoder model provides high robustness in a multi-step prediction and can predict the gas concentration for five different time steps. Its prediction error is significantly lower than those of the ARMA and the CHAOS models. The prediction accuracy is further improved through a fusion with information of other sensors. In this way, this study provides a novel concept and method for gas accident prevention.

中文翻译：

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基于 LSTM 的编码器-解码器，用于使用多传感器融合进行气体浓度短期预测

摘要 瓦斯是煤矿中最危险的副产品之一。在瓦斯事故发生之前，可以观察到气体浓度异常升高。因此，预测煤矿瓦斯浓度对于预防瓦斯事故、保障煤矿安全生产具有重要意义。通过计算不同传感器瓦斯浓度的皮尔逊相关系数，验证了每个工作面监测的瓦斯浓度的空间相关性。我们提出了基于 ARMA 模型、CHAOS 模型和 Encoder-Decoder 模型（单传感器和多传感器）的气体浓度时间序列的多步预测结果，并比较了这些结果。Encoder-Decoder 模型在多步预测中提供了高度的鲁棒性，并且可以预测五个不同时间步的气体浓度。其预测误差明显低于 ARMA 和 CHAOS 模型。通过与其他传感器的信息融合，进一步提高预测精度。通过这种方式，本研究为预防瓦斯事故提供了一种新的概念和方法。