Direct Prediction of the Toxic Gas Diffusion Rule in a Real Environment Based on LSTM

Fei Qian, Li Chen, Jun Li, Chao Ding, Xianfu Chen and Jian Wang
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Fei Qian: Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230029, China
Li Chen: Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230029, China
Jun Li: Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230029, China
Chao Ding: State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230029, China
Xianfu Chen: Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230029, China
Jian Wang: State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230029, China

IJERPH, 2019, vol. 16, issue 12, 1-14

Abstract: Predicting the diffusion rule of toxic gas plays a distinctly important role in emergency capability assessment and rescue work. Among diffusion prediction models, the traditional artificial neural network has exhibited excellent performance not only in prediction accuracy but also in calculation time. Nevertheless, with the continuous development of deep learning and data science, some new prediction models based on deep learning algorithms have been shown to be more advantageous because their structure can better discover internal laws and external connections between input data and output data. The long short-term memory (LSTM) network is a kind of deep learning neural network that has demonstrated outstanding achievements in many prediction fields. This paper applies the LSTM network directly to the prediction of toxic gas diffusion and uses the Project Prairie Grass dataset to conduct experiments. Compared with the Gaussian diffusion model, support vector machine (SVM) model, and back propagation (BP) network model, the LSTM model of deep learning has higher prediction accuracy (especially for the prediction at the point of high concentration values) while avoiding the occurrence of negative concentration values and overfitting problems found in traditional artificial neural network models.

Keywords: toxic gas; diffusion prediction models; deep learning algorithms; LSTM (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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