Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure lines
Abstract The construction of transportation infrastructure lines alters the natural terrain, leading to uneven redistribution of snow under the influence of snow-drifting, which can compromise road safety. With the ongoing expansion of transportation networks in cold regions, transportation engineer...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86207-4 |
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| author | Pengxiang Li Aiying Gao Mingzhou Bai Shumao Qiu |
| author_facet | Pengxiang Li Aiying Gao Mingzhou Bai Shumao Qiu |
| author_sort | Pengxiang Li |
| collection | DOAJ |
| description | Abstract The construction of transportation infrastructure lines alters the natural terrain, leading to uneven redistribution of snow under the influence of snow-drifting, which can compromise road safety. With the ongoing expansion of transportation networks in cold regions, transportation engineers are increasingly focusing on snow-drifting phenomena and the associated disasters. This study, based on a GIS platform, conducts a case study of a transport infrastructure project in Xinjiang to analyze the geographical and environmental conditions along the route. Through on-site monitoring and investigation, factors related to the occurrence of snow-drifting disasters were implied. The WOE (Weight of Evidence) model was selected as the base evaluation model, and the BP-GA algorithm was applied to optimize the weights of evaluation indicators. This led to the establishment of a susceptibility evaluation index system for snow-drifting disasters, improving both the computational efficiency and the accuracy of the evaluation model. The results indicate that the evaluation accuracies of the WOE, WOE-BP, and WOE-BP-GA models were 72.32%, 75.32%, and 85.18%, respectively. The use of the GA-BP algorithm effectively captured the complex nonlinear relationships among various factors, producing evaluation results highly consistent with real-world conditions. This method may efficiently identify high-risk areas of snow-drifting along transportation infrastructure lines, providing valuable insights for disaster prevention using ArcGIS. |
| format | Article |
| id | doaj-art-13e672ce8a10423d9c9f0ce8da49b835 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-13e672ce8a10423d9c9f0ce8da49b8352025-01-19T12:17:59ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-025-86207-4Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure linesPengxiang Li0Aiying Gao1Mingzhou Bai2Shumao Qiu3Transportation Development Research Center, China Academy of Transportation SciencesTransportation Development Research Center, China Academy of Transportation SciencesSchool of Civil Engineering, Beijing Jiaotong UniversityResearch Institute of Highway, Ministry of TransportAbstract The construction of transportation infrastructure lines alters the natural terrain, leading to uneven redistribution of snow under the influence of snow-drifting, which can compromise road safety. With the ongoing expansion of transportation networks in cold regions, transportation engineers are increasingly focusing on snow-drifting phenomena and the associated disasters. This study, based on a GIS platform, conducts a case study of a transport infrastructure project in Xinjiang to analyze the geographical and environmental conditions along the route. Through on-site monitoring and investigation, factors related to the occurrence of snow-drifting disasters were implied. The WOE (Weight of Evidence) model was selected as the base evaluation model, and the BP-GA algorithm was applied to optimize the weights of evaluation indicators. This led to the establishment of a susceptibility evaluation index system for snow-drifting disasters, improving both the computational efficiency and the accuracy of the evaluation model. The results indicate that the evaluation accuracies of the WOE, WOE-BP, and WOE-BP-GA models were 72.32%, 75.32%, and 85.18%, respectively. The use of the GA-BP algorithm effectively captured the complex nonlinear relationships among various factors, producing evaluation results highly consistent with real-world conditions. This method may efficiently identify high-risk areas of snow-drifting along transportation infrastructure lines, providing valuable insights for disaster prevention using ArcGIS.https://doi.org/10.1038/s41598-025-86207-4Transportation infrastructure lineSnow-driftingEvaluation modelNeural networkSusceptibility |
| spellingShingle | Pengxiang Li Aiying Gao Mingzhou Bai Shumao Qiu Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure lines Scientific Reports Transportation infrastructure line Snow-drifting Evaluation model Neural network Susceptibility |
| title | Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure lines |
| title_full | Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure lines |
| title_fullStr | Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure lines |
| title_full_unstemmed | Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure lines |
| title_short | Evaluation of snow-drifting influencing factors and susceptibility of transportation infrastructure lines |
| title_sort | evaluation of snow drifting influencing factors and susceptibility of transportation infrastructure lines |
| topic | Transportation infrastructure line Snow-drifting Evaluation model Neural network Susceptibility |
| url | https://doi.org/10.1038/s41598-025-86207-4 |
| work_keys_str_mv | AT pengxiangli evaluationofsnowdriftinginfluencingfactorsandsusceptibilityoftransportationinfrastructurelines AT aiyinggao evaluationofsnowdriftinginfluencingfactorsandsusceptibilityoftransportationinfrastructurelines AT mingzhoubai evaluationofsnowdriftinginfluencingfactorsandsusceptibilityoftransportationinfrastructurelines AT shumaoqiu evaluationofsnowdriftinginfluencingfactorsandsusceptibilityoftransportationinfrastructurelines |