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

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

Wang, Rongxiao (National University of Defense Technology)
Chen, B. (National University of Defense Technology)
Qiu, S. (TU Delft Web Information Systems; National University of Defense Technology)
Zhu, Zhengqiu (National University of Defense Technology)
Wang, Yiduo (National University of Defense Technology)
Wang, Yiping (Naval 902 Factory)
Qiu, Xiaogang (National University of Defense Technology)

2018

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.

Back propagation network
Field case
Hazardous gas dispersion prediction
Input selection
Support vector regression

http://resolver.tudelft.nl/uuid:e681175a-09f1-4ad3-a599-fd2cd876a9d7

https://doi.org/10.3390/ijerph15071450

1661-7827

International Journal of Environmental Research and Public Health, 15 (7), 1-19

Institutional Repository

journal article

© 2018 Rongxiao Wang, B. Chen, S. Qiu, Zhengqiu Zhu, Yiduo Wang, Yiping Wang, Xiaogang Qiu