Accurate and rapid toxic gas concentration prediction model plays an important role in emergency aid of sudden gas leak. However, it is difficult for existing dispersion model to achieve accuracy and efficiency requirements at the same time. Although some researchers have considered developing new forecasting models with traditional machine learning, such as back propagation (BP) neural network, support vector machine (SVM), the prediction results obtained from such models need to be improved still in terms of accuracy. Then new prediction models based on deep learning are proposed in this paper. Deep learning has obvious advantages over traditional machine learning in prediction and classification. Deep belief networks (DBNs) as well as convolution neural networks (CNNs) are used to build new dispersion models here. Both models are compared with Gaussian plume model, computation fluid dynamics (CFD) model and models based on traditional machine learning in terms of accuracy, prediction time, and computation time. The experimental results turn out that CNNs model performs better considering all evaluation indexes.

准确，快速的有毒气体浓度预测模型在突发性气体泄漏应急救助中起着重要作用。但是，现有的色散模型很难同时达到精度和效率要求。尽管一些研究人员已经考虑使用传统的机器学习方法开发新的预测模型，例如反向传播（BP）神经网络，支持向量机（SVM），但仍需要在准确性方面改进从此类模型获得的预测结果。然后提出了基于深度学习的新预测模型。深度学习在预测和分类方面比传统机器学习具有明显优势。深度信念网络（DBN）和卷积神经网络（CNN）在这里用于建立新的色散模型。在准确性，预测时间和计算时间方面，将这两种模型与高斯羽流模型，计算流体动力学（CFD）模型和基于传统机器学习的模型进行了比较。实验结果表明，考虑所有评估指标，CNNs模型的效果更好。