# Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City
## 5 Conclusions  
This paper proposed a sensor-based fully connected model trained with a hybrid strategy to track a leaking source in chemical parks within an urban region of 2 km \(\times\) 2 km. The forward gas diffusion model is AERMOD with complex terrain simulated. It takes many weather parameters into consideration such as varying wind direction, wind speed, temperature, and fixed total cloud cover and low cloud cover. However, the real atmospheric situation is much more complex than the simulation, so there is still a long way to go in handling the diffusion process.  
Utilizing a refined hybrid training strategy, the proposed source tracking model achieves a final accuracy of 99.14% classifying 79 dispersed sources using only 61 gas concentrations as input. Our proposed model performs well without prior information such as wind speed and direction. Except for two sources, whose tracking accuracy is 91% and 92%, the others are all above 95%, as shown in Figure 11. Figure 12 shows the average and standard deviation of the accuracy, recall, and \(F1\)-_score_ achieved by our best model, the model with 10% sensor failure, and the model with \(\pm 1\%\) random perturbation. The definition of standard deviation is shown below:  
\[Stdev=\sqrt{\frac{\sum_{i=1}^{n}(Acc_{i}-\overline{Acc})^{2}}{n-1}} \tag{15}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Source No.** & **Precision** & **Recall** & **\(F1\)-_score_** \\ \hline
25 & 0.97 & 0.97 & 0.97 \\
27 & 0.97 (+0.01) & 0.97 & 0.97 \\
38 & 0.97 & 0.97 & 0.97 \\
39 & 0.98 & 0.98 & 0.98 \\
40\({}^{\circ}\) & 0.93 (+0.01) & 0.92 (\(-\)0.01) & 0.93 \\
41\({}^{\circ}\) & 0.92 (\(-\)0.01) & 0.93 (+0.01) & 0.93 (+0.01) \\
42 & 0.98 & 0.98 & 0.98 \\
44 & 0.97 (+0.01) & 0.97 & 0.97 (+0.01) \\
46 & 0.97 & 0.97 (+0.01) & 0.97 \\
47 & 0.96 (\(-\)0.01) & 0.97 & 0.97 \\
50 & 0.98 (+0.01) & 0.97 & 0.97 \\
51 & 0.96 (\(-\)0.01) & 0.97 & 0.97 \\
52\({}^{\circ}\) & 0.89 (+0.01) & 0.90 (+0.01) & 0.89 \\
53\({}^{\circ}\) & 0.89 (+0.01) & 0.89 & 0.89 (+0.01) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance after applying focal loss as the loss function. Sources with indicator values less than 0.95 are marked by ‘-’.  
Figure 11: The proportion of sources whose classification accuracy were greater than 90%, 95%, 97%, and 99% under three different conditions.  
Even with a 10% sensor failure probability or \(\pm\)1% random perturbation, the corresponding results still show the effectiveness and robustness of our proposed model.  
Although the source release rate is fixed in our dataset, with the introduction of the normalization method in the second training stage, the proposed model is able to handle this issue to some extent. Once a gas leakage event occurs, relevant departments can rapidly and accurately track the location of the leaking source through this model.  
Conceptualization, S.H.; methodology, J.L.; software, J.L. and Z.Z.; validation, J.L.; formal analysis, J.L.; investigation, J.L.; resources, S.H.; data curation, J.L. and T.M.; writing--original draft preparation, J.L.; writing--review and editing, J.L. and S.H.; visualization, J.L.; supervision, S.H.; project administration, S.H.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Zhejiang Province (grant number 2021C03178), \"Pioneer\" and \"Leading Goose\" R&D Program of Zhejiang Province (2023C03135), and the National Natural Science Foundation of China (11621101) and the Ningbo Science and Technology Project under Grant (2021Z076).  
Data Availability StatementData sharing is not applicable to this article as the data involves national surveying and mapping geographic information.  
The authors are grateful to [PERSON], [PERSON] of Zhejiang University and [PERSON], [PERSON] of Sanjie Environmental Engineering Consulting (Hangzhou) Co., Ltd. for valuable discussions and helps.  
The authors declare no conflict of interest.