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RSS FeedsIJERPH, Vol. 16, Pages 2133: Direct Prediction of the Toxic Gas Diffusion Rule in a Real Environment Based on LSTM (International Journal of Environmental Research and Public Health)

 
 

17 june 2019 13:00:24

 
IJERPH, Vol. 16, Pages 2133: Direct Prediction of the Toxic Gas Diffusion Rule in a Real Environment Based on LSTM (International Journal of Environmental Research and Public Health)
 




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.


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44 viewsCategory: Medicine, Pathology, Toxicology
 
IJERPH, Vol. 16, Pages 2134: Sociodemographic Associations of Physical Activity in People of Working Age (International Journal of Environmental Research and Public Health)
IJERPH, Vol. 16, Pages 2144: Modal Shift from Cars and Promotion of Walking by Providing Pedometers in Yokohama City, Japan (International Journal of Environmental Research and Public Health)
 
 
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