| Citation: | GU Shuang, CHENG Guo-zhu, YAN Dong-yang. Prediction of potential risk paths in natural disaster network along railway lines based on text mining[J]. Journal of Traffic and Transportation Engineering, 2026, 26(5): 260-274. doi: 10.19818/j.cnki.1671-1637.2026.121
|
To reveal the chain propagation mechanism of natural disasters along railway lines, quantify their threats to railway transportation safety, and analyze the risk evolution laws, a prediction model for natural disaster risk paths along railway lines based on text mining and complex networks was constructed. Based on the historical disaster text dataset along railway lines, the text mining technology was improved; the bag-of-words model was optimized, and combined with the keyword extraction method, core elements such as railway stations, disaster-causing factors, consequences, and severity levels in the disaster data were accurately extracted; based on complex network theory, a heterogeneous network of railway disaster risks was established, with keywords as nodes (3 types of entities) and co-occurrence semantic associations in the same event as edges (6 types of relationships); a multi-path search algorithm was designed to traverse the network topology, and the associated co-occurrence matrix was integrated to quantify the risk transmission intensity between nodes, realizing the procedural mining of multiple types of propagation paths. The analysis results show that the receiver operating characteristic curve of the model is close to the upper left corner; the area under the curve is 0.938; the accuracy reaches 94.873%, and the F1 score is 0.899; the risk transmission values between node pairs are quantitatively output, and high-probability disaster chains are successfully located as: Markam Station → heavy snowfall → Shangri-La Station → personnel disaster → power equipment damage (risk value 0.866), and Jomda Station → Gonjo Station → severe convective weather → casualties (risk value 0.841). The obtained model constructs a disaster semantic network driven by text, which can achieve the quantitative prediction of railway risk paths and the identification of key transmission chains, accurately locate the high-incidence links of secondary disasters, and provide support for the proactive risk prevention and control of railways in complex environments.
| [1] |
LU C F, CAI C X. Challenges and countermeasures for construction safety during the Sichuan-Tibet Railway project[J]. Engineering, 2019, 5: 833-838. doi: 10.1016/j.eng.2019.06.007
|
| [2] |
LI Yuan-yuan, MEI Hong-bo, REN Xiao-jie, et al. Geological disaster susceptibility evaluation based on certainty factor and support vector machine[J]. Geo-Information Science, 2018, 20(12): 1699-1709.
|
| [3] |
XIA Xi-man, MENG Xue-lei, CHENG Xiao-qing, et al. High-speed railway risk prediction based on internal and external perspectives[J]. Journal of Railway Science and Engineering, 2025, 22(7): 2921-2931.
|
| [4] |
WANG Zhe-tao, CHEN Ru-hai, WEI Guo-jun. Significance of risk assessment of geological hazards in railway construction[J]. Journal of Gansu Sciences, 2003, 15: 72-75.
|
| [5] |
LAN T W, FAN C J, LI S, et al. Probabilistic prediction of mine dynamic disaster risk based on multiple factor pattern recognition[J]. Advances in Civil Engineering, 2018, 2018: 7813931. doi: 10.1155/2018/7813931
|
| [6] |
SONG Zhe, GUO Zhan, XI Nian-sheng, et al. Research on railway safety risk transfer network model based on probability analysis[J]. Railway Transport and Economy, 2024, 46(4): 142-152.
|
| [7] |
WU Jing-jing, JIANG Si-yi, WU Qiu-ju, et al. Landslide geological hazard vulnerability evaluation based on GIS and BP neural network[J]. Resource Information and Engineering, 2021, 36(4): 100-107.
|
| [8] |
WANG X D, ZHANG C B, WANG C, et al. GIS-based for prediction and prevention of environmental geological disaster susceptibility: From a perspective of sustainable development[J]. Ecotoxicology and Environmental Safety, 2021, 226: 112881. doi: 10.1016/j.ecoenv.2021.112881
|
| [9] |
FU Hong-en, GAO Yi-ju, FENG Ying-ying, et al. Hazard prediction of urban rainstorm and flood disasters based on GA-SVR-C model: Case study of Shenzhen City[J]. Yangtze River, 2021, 52(8): 16-21.
|
| [10] |
SONG Guo-ce, WANG Gao-lei, LU Da-wei, et al. Risk evaluation model of safety hazards in railway external environment based on ISM-ANP and its application[J]. Railway Transport and Economy, 2024, 46(6): 161-168.
|
| [11] |
GONG Yan-bing, XIANG Lin, LIU Gao-feng. Study on flood disaster loss prediction based on gaussian process regression model: A case study of Chongqing city[J]. Resources and Environment in the Yangtze Basin, 2019, 28(6): 1502-1510.
|
| [12] |
CAO Y, YIN K D, LI X M. Prediction of direct economic loss caused by marine disasters based on the improved GM (1, 1) model[J]. Journal of Grey System, 2020, 32(1): 133-145.
|
| [13] |
LI Bo, FENG Qiao-bin, QI Ke-wei. Economic loss prediction of meteorological disaster based on improved neural network model: Taking the Guangdong typhoon as an example[J]. Journal of Chongqing University of Technology(Natural Science), 2021, 35(4): 247-253.
|
| [14] |
ZHANG Y, HAO Y H. Loss prediction of mountain flood disaster in villages and towns based on rough set RBF neural network[J]. Neural Computing and Applications, 2022, 34(5): 2513-2524.
|
| [15] |
HUANG X, SONG J Y, JIN H D. The casualty prediction of earthquake disaster based on extreme learning machine method[J]. Natural Hazards, 2020, 102: 873-886. doi: 10.1007/s11069-020-03937-6
|
| [16] |
HU Zhong-jun, LIU Yan-qiu, LI Jia. Dynamic demand forecast of emergency materials for flood disasters based on improved GM(1, 1) model[J]. Journal of System Simulation, 2019, 31(4): 702-709.
|
| [17] |
LIU Fang, FENG Dan, GONG Xue-ran. Demand predicting of emergency supplies for flood disaster based on IACO-BP algorithm[J]. Journal of Shenyang University of Technology, 2019, 41(3): 332-338.
|
| [18] |
HOU L, WU X G, WU Z, et al. Pattern identification and risk prediction of domino effect based on data mining methods for accidents occurred in the tank farm[J]. Reliability Engineering and System Safety, 2020, 193: 106646. doi: 10.1016/j.ress.2019.106646
|
| [19] |
XU Jia, DING Chao, ZHANG Xin-yu, et al. Review of complex network-based urban waterlogging disaster emergency management research[J]. Journal of Natural Disasters, 2024, 33(3): 1-16.
|
| [20] |
MA Chao-qun, ZHANG Shuang, CHEN Quan, et al. Characteristics and vulnerability of rail transit network based on perspective of passenger flow characteristic[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 208-216. doi: 10.19818/j.cnki.1671-1637.2020.05.017
|
| [21] |
LÜ L Y, ZHOU T. Link prediction in complex networks: A survey[J]. Physica A: Statistical Mechanics and its Applications, 2011, 390: 1150-1170. doi: 10.1016/j.physa.2010.11.027
|
| [22] |
HU Zuo-an, DENG Jin-cheng, HAN Jin-li, et al. Review on application of graph neural network in traffic prediction[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 39-61. doi: 10.19818/j.cnki.1671-1637.2023.05.003
|
| [23] |
ZHAO Xiao-li, SU Yun. Research on public concern of disaster information based on Weibo data: Take the rainstorm flood event in Henan Province as an example[J]. Journal of Natural Disasters, 2024, 33(1): 89-98.
|
| [24] |
YAN D Y, LI K P, GU S, et al. Network-based bag-of-words model for text classification[J]. IEEE Access, 2020, 8: 99.
|
| [25] |
YANG L, LI K P, ZHAO D, et al. A network method for identifying the root cause of high-speed rail faults based on text data[J]. Energies, 2019, 12(10): 1908. doi: 10.3390/en12101908
|
| [26] |
LI Man, LIU Gui-yuan, WANG Yan-hui, et al. Review of risk evolution research and its application and discussion in rail transit[J]. Railway Transport and Economy, 2025, 47(1): 13-30.
|
| [27] |
PECH R, HAO D, PAN L M, et al. Link prediction via matrix completion[J]. Europhysics Letters, 2017, 117(3): 38002. doi: 10.1209/0295-5075/117/38002
|
| [28] |
LI K P, GU S, YAN D Y. A link prediction method based on neural networks[J]. Applied Sciences, 2021, 11: 5186. doi: 10.3390/app11115186
|
| [29] |
PAN Li-ming. Studies on link prediction and information spreading in complex networks[D]. Chengdu: School of Computer Science & Engineering, 2019.
|
Website copyright Journal of Traffic and Transportation Engineering Editorial Department陕ICP备05001904号-1
Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang'an University, Middle Section of South 2nd Ring Road, Xi'an, China（710064） Tel：029-82334388 Email：jygc@chd.edu.cn
All visit:Today's visit:
Supported by:
Beijing Renhe Information Technology Co. Ltd
DownLoad:
