Toxic gas dispersion prediction for point source emission using deep learning method. Issue 2 (7th February 2020)

• Record Type: Journal Article
• Authors: Ni, Jing; Yang, Hongbing; Yao, Jun; Li, Zhiying; Qin, Ping
• Is Part Of: Human & ecological risk assessment. Volume 26:Issue 2(2020)
• Page Start: 557
• Page End: 570
• Publication Date: 2020-02-07
• Subjects: Toxic gas -- dispersion model -- deep learning; Health risk assessment -- Periodicals; Ecological risk assessment -- Periodicals; Ecology -- Periodicals; Risk Assessment -- Periodicals; 363.102
• DOI: 10.1080/10807039.2018.1526632
• Languages: English
• ISSNs: 1080-7039

Abstract

Abstract: 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.