Research and Application of Early Identification of Geological Hazards Technology in Railway Disaster Prevention and Control: A Case Study of Southeastern Gansu, China

Geological hazards significantly threaten the safety of China’s railway network. As the railway system continues to expand, particularly with the effects of accelerated climate change, approximately 70% of the newly encountered geohazards occur outside of known areas. This study proposes a novel app...
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Autor*in:	Peng He [verfasserIn] Zhaocheng Guo [verfasserIn] Hong Chen [verfasserIn] Pengqing Shi [verfasserIn] Xiaolong Zhou [verfasserIn] Genhou Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	RS GIS early identification of geohazards Gansu railway network

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 15(2023), 24, p 16705
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:24, p 16705

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su152416705

Katalog-ID:	DOAJ098793659

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allfieldsSound	10.3390/su152416705 doi (DE-627)DOAJ098793659 (DE-599)DOAJ23d74a58f8e144899f03e1b71c346b27 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Peng He verfasserin aut Research and Application of Early Identification of Geological Hazards Technology in Railway Disaster Prevention and Control: A Case Study of Southeastern Gansu, China 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Geological hazards significantly threaten the safety of China’s railway network. As the railway system continues to expand, particularly with the effects of accelerated climate change, approximately 70% of the newly encountered geohazards occur outside of known areas. This study proposes a novel approach that can be applied to railway systems to identify potential geohazards, analyze risk areas, and assess section vulnerability. The methodology uses integrated remote sensing technology to effectively enhance potential railway hazard identification timeliness. It combines kernel density, hotspot, and inverse distance-weighted analysis methods to enhance applicability and accuracy in the risk assessment of railway networks. Using a case study in southeastern Gansu as an example, we identified 3976 potential hazards in the study area, analyzed five areas with high concentrations of hazards, and 11 districts and counties prone to disasters that could threaten the railway network. We accurately located 16 sections and 20 significant landslide hazards on eight railway lines that pose operational risks. The effectiveness of the methodology proposed in this paper has been confirmed through field investigations of significant landslide hazards. This study can provide a scientific basis for the sustainability of the railway network and disaster risk management. RS GIS early identification of geohazards Gansu railway network Environmental effects of industries and plants Renewable energy sources Environmental sciences Zhaocheng Guo verfasserin aut Hong Chen verfasserin aut Pengqing Shi verfasserin aut Xiaolong Zhou verfasserin aut Genhou Wang verfasserin aut In Sustainability MDPI AG, 2009 15(2023), 24, p 16705 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:15 year:2023 number:24, p 16705 https://doi.org/10.3390/su152416705 kostenfrei https://doaj.org/article/23d74a58f8e144899f03e1b71c346b27 kostenfrei https://www.mdpi.com/2071-1050/15/24/16705 kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 24, p 16705
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source	In Sustainability 15(2023), 24, p 16705 volume:15 year:2023 number:24, p 16705
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callnumber-first	T - Technology
author	Peng He
spellingShingle	Peng He misc TD194-195 misc TJ807-830 misc GE1-350 misc RS misc GIS misc early identification of geohazards misc Gansu railway network misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Research and Application of Early Identification of Geological Hazards Technology in Railway Disaster Prevention and Control: A Case Study of Southeastern Gansu, China
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topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc RS misc GIS misc early identification of geohazards misc Gansu railway network misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	Research and Application of Early Identification of Geological Hazards Technology in Railway Disaster Prevention and Control: A Case Study of Southeastern Gansu, China
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title_full	Research and Application of Early Identification of Geological Hazards Technology in Railway Disaster Prevention and Control: A Case Study of Southeastern Gansu, China
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title_sort	research and application of early identification of geological hazards technology in railway disaster prevention and control: a case study of southeastern gansu, china
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title_auth	Research and Application of Early Identification of Geological Hazards Technology in Railway Disaster Prevention and Control: A Case Study of Southeastern Gansu, China
abstract	Geological hazards significantly threaten the safety of China’s railway network. As the railway system continues to expand, particularly with the effects of accelerated climate change, approximately 70% of the newly encountered geohazards occur outside of known areas. This study proposes a novel approach that can be applied to railway systems to identify potential geohazards, analyze risk areas, and assess section vulnerability. The methodology uses integrated remote sensing technology to effectively enhance potential railway hazard identification timeliness. It combines kernel density, hotspot, and inverse distance-weighted analysis methods to enhance applicability and accuracy in the risk assessment of railway networks. Using a case study in southeastern Gansu as an example, we identified 3976 potential hazards in the study area, analyzed five areas with high concentrations of hazards, and 11 districts and counties prone to disasters that could threaten the railway network. We accurately located 16 sections and 20 significant landslide hazards on eight railway lines that pose operational risks. The effectiveness of the methodology proposed in this paper has been confirmed through field investigations of significant landslide hazards. This study can provide a scientific basis for the sustainability of the railway network and disaster risk management.
abstractGer	Geological hazards significantly threaten the safety of China’s railway network. As the railway system continues to expand, particularly with the effects of accelerated climate change, approximately 70% of the newly encountered geohazards occur outside of known areas. This study proposes a novel approach that can be applied to railway systems to identify potential geohazards, analyze risk areas, and assess section vulnerability. The methodology uses integrated remote sensing technology to effectively enhance potential railway hazard identification timeliness. It combines kernel density, hotspot, and inverse distance-weighted analysis methods to enhance applicability and accuracy in the risk assessment of railway networks. Using a case study in southeastern Gansu as an example, we identified 3976 potential hazards in the study area, analyzed five areas with high concentrations of hazards, and 11 districts and counties prone to disasters that could threaten the railway network. We accurately located 16 sections and 20 significant landslide hazards on eight railway lines that pose operational risks. The effectiveness of the methodology proposed in this paper has been confirmed through field investigations of significant landslide hazards. This study can provide a scientific basis for the sustainability of the railway network and disaster risk management.
abstract_unstemmed	Geological hazards significantly threaten the safety of China’s railway network. As the railway system continues to expand, particularly with the effects of accelerated climate change, approximately 70% of the newly encountered geohazards occur outside of known areas. This study proposes a novel approach that can be applied to railway systems to identify potential geohazards, analyze risk areas, and assess section vulnerability. The methodology uses integrated remote sensing technology to effectively enhance potential railway hazard identification timeliness. It combines kernel density, hotspot, and inverse distance-weighted analysis methods to enhance applicability and accuracy in the risk assessment of railway networks. Using a case study in southeastern Gansu as an example, we identified 3976 potential hazards in the study area, analyzed five areas with high concentrations of hazards, and 11 districts and counties prone to disasters that could threaten the railway network. We accurately located 16 sections and 20 significant landslide hazards on eight railway lines that pose operational risks. The effectiveness of the methodology proposed in this paper has been confirmed through field investigations of significant landslide hazards. This study can provide a scientific basis for the sustainability of the railway network and disaster risk management.
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title_short	Research and Application of Early Identification of Geological Hazards Technology in Railway Disaster Prevention and Control: A Case Study of Southeastern Gansu, China
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Research and Application of Early Identification of Geological Hazards Technology in Railway Disaster Prevention and Control: A Case Study of Southeastern Gansu, China

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