Comparison of Machine Learning Models for Hazardous Gas Dispersion Prediction in Field Cases

Rongxiao Wang, Bin Chen, Sihang Qiu, Zhengqiu Zhu, Yiduo Wang, Yiping Wang and Xiaogang Qiu
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Rongxiao Wang: College of System Engineering, National University of Defense Technology, 109 Deya Road, Changsha 410073, China
Bin Chen: College of System Engineering, National University of Defense Technology, 109 Deya Road, Changsha 410073, China
Sihang Qiu: College of System Engineering, National University of Defense Technology, 109 Deya Road, Changsha 410073, China
Zhengqiu Zhu: College of System Engineering, National University of Defense Technology, 109 Deya Road, Changsha 410073, China
Yiduo Wang: College of System Engineering, National University of Defense Technology, 109 Deya Road, Changsha 410073, China
Yiping Wang: The Naval 902 Factory, Shanghai 200083, China
Xiaogang Qiu: College of System Engineering, National University of Defense Technology, 109 Deya Road, Changsha 410073, China

IJERPH, 2018, vol. 15, issue 7, 1-19

Abstract: 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 can be summarized that the SVR shows better generalization ability with relatively small training sets, but tends to under-fit the training data. In contrast, the BP network has a stronger fitting ability, but sometimes suffers from an over-fitting problem. As a result, the model and input selection presented in this paper will be of great help to environmental and public health protection in real applications.

Keywords: hazardous gas dispersion prediction; back propagation network; support vector regression; input selection; field case (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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