IJERPH, Vol. 15, Pages 1450: Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases

IJERPH, Vol. 15, Pages 1450: Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases International Journal of Environmental Research and Public Health doi: 10.3390/ijerph15071450 Authors: Rongxiao Wang Bin Chen Sihang Qiu Zhengqiu Zhu Yiduo Wang Yiping Wang Xiaogang Qiu Dispersion prediction plays a significant role in the management and emergency response to hazardous gas emissions and accidental leaks. Compared with conventional atmospheric dispersion models, machine leaning (ML) models have both high accuracy and efficiency in terms of prediction, especially in field cases. However, selection of model type and the inputs of the ML model are still essential problems. To address this issue, two ML models (i.e., the back propagation (BP) network and support vector regression (SVR) with different input selections (i.e., original monitoring parameters and integrated Gaussian parameters) are proposed in this paper. To compare the performances of presented ML models in field cases, these models are evaluated using the Prairie Grass and Indianapolis field data sets. The influence of the training set scale on the performances of ML models is analyzed as well. Results demonstrate that the integrated Gaussian parameters indeed improve the prediction accuracy in the Prairie Grass case. However, they do not make much difference in the Indianapolis case due to their inadaptability to the complex terrain conditions. In addition, it...
Source: International Journal of Environmental Research and Public Health - Category: Environmental Health Authors: Tags: Article Source Type: research