Prediction of Potential Geothermal Disaster Areas along the Yunnan–Tibet Railway Project

As China’s railways continue to expand into the Qinghai–Tibet Plateau, the number of deep-buried long tunnels is increasing. Tunnel-damaging geothermal disasters have become a common problem in underground engineering. Predicting the potential geothermal disaster areas along the Yunnan–Tibet railway...
Ausführliche Beschreibung

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Autor*in:	Zhe Chen [verfasserIn] Ruichun Chang [verfasserIn] Huadong Guo [verfasserIn] Xiangjun Pei [verfasserIn] Wenbo Zhao [verfasserIn] Zhengbo Yu [verfasserIn] Lu Zou [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Yunnan–Tibet railway geothermal disaster sustainable development Landsat-8 niche model

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 14(2022), 13, p 3036
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:13, p 3036

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs14133036

Katalog-ID:	DOAJ036855057

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spelling	10.3390/rs14133036 doi (DE-627)DOAJ036855057 (DE-599)DOAJ3e0591b3aa09454399dc9b5543a6cd29 DE-627 ger DE-627 rakwb eng Zhe Chen verfasserin aut Prediction of Potential Geothermal Disaster Areas along the Yunnan–Tibet Railway Project 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As China’s railways continue to expand into the Qinghai–Tibet Plateau, the number of deep-buried long tunnels is increasing. Tunnel-damaging geothermal disasters have become a common problem in underground engineering. Predicting the potential geothermal disaster areas along the Yunnan–Tibet railway project is conducive to its planning and construction and the realization of the United Nations Sustainable Development Goals (SDGs)—specifically, the industry, innovation and infrastructure goal (SDG 9). In this paper, the Yunnan–Tibet railway project was the study area. Landsat-8 images and other spatial data were used to investigate causes and distributions of geothermal disasters. A collinearity diagnosis of environmental variables was carried out. Twelve environmental variables, such as land surface temperature, were selected to predict potential geothermal disaster areas using four niche models (MaxEnt, Bioclim, Domain and GARP). The prediction results were divided into four levels and had different characteristics. Among them, the area under receiver operating characteristic curve (AUC) and kappa values of the MaxEnt model were the highest, at 0.84 and 0.63, respectively. Its prediction accuracy was the highest and the algorithm results are more suitable for the prediction of geothermal disasters. The prediction results show that the geothermal disaster potential is greatest in the Markam-Deqen, Zuogong-Zayu and Baxoi-Zayu regions. Through jack-knife analysis, it was found that the land surface temperature, active faults, water system distribution and Moho depth are the key environmental predictors of potential geothermal disaster areas. The research results provide a reference for the design and construction of the Yunnan–Tibet railway project and associated sustainable development. Yunnan–Tibet railway geothermal disaster sustainable development Landsat-8 niche model Science Q Ruichun Chang verfasserin aut Huadong Guo verfasserin aut Xiangjun Pei verfasserin aut Wenbo Zhao verfasserin aut Zhengbo Yu verfasserin aut Lu Zou verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 13, p 3036 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:13, p 3036 https://doi.org/10.3390/rs14133036 kostenfrei https://doaj.org/article/3e0591b3aa09454399dc9b5543a6cd29 kostenfrei https://www.mdpi.com/2072-4292/14/13/3036 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 13, p 3036
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allfieldsGer	10.3390/rs14133036 doi (DE-627)DOAJ036855057 (DE-599)DOAJ3e0591b3aa09454399dc9b5543a6cd29 DE-627 ger DE-627 rakwb eng Zhe Chen verfasserin aut Prediction of Potential Geothermal Disaster Areas along the Yunnan–Tibet Railway Project 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As China’s railways continue to expand into the Qinghai–Tibet Plateau, the number of deep-buried long tunnels is increasing. Tunnel-damaging geothermal disasters have become a common problem in underground engineering. Predicting the potential geothermal disaster areas along the Yunnan–Tibet railway project is conducive to its planning and construction and the realization of the United Nations Sustainable Development Goals (SDGs)—specifically, the industry, innovation and infrastructure goal (SDG 9). In this paper, the Yunnan–Tibet railway project was the study area. Landsat-8 images and other spatial data were used to investigate causes and distributions of geothermal disasters. A collinearity diagnosis of environmental variables was carried out. Twelve environmental variables, such as land surface temperature, were selected to predict potential geothermal disaster areas using four niche models (MaxEnt, Bioclim, Domain and GARP). The prediction results were divided into four levels and had different characteristics. Among them, the area under receiver operating characteristic curve (AUC) and kappa values of the MaxEnt model were the highest, at 0.84 and 0.63, respectively. Its prediction accuracy was the highest and the algorithm results are more suitable for the prediction of geothermal disasters. The prediction results show that the geothermal disaster potential is greatest in the Markam-Deqen, Zuogong-Zayu and Baxoi-Zayu regions. Through jack-knife analysis, it was found that the land surface temperature, active faults, water system distribution and Moho depth are the key environmental predictors of potential geothermal disaster areas. The research results provide a reference for the design and construction of the Yunnan–Tibet railway project and associated sustainable development. Yunnan–Tibet railway geothermal disaster sustainable development Landsat-8 niche model Science Q Ruichun Chang verfasserin aut Huadong Guo verfasserin aut Xiangjun Pei verfasserin aut Wenbo Zhao verfasserin aut Zhengbo Yu verfasserin aut Lu Zou verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 13, p 3036 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:13, p 3036 https://doi.org/10.3390/rs14133036 kostenfrei https://doaj.org/article/3e0591b3aa09454399dc9b5543a6cd29 kostenfrei https://www.mdpi.com/2072-4292/14/13/3036 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 13, p 3036
allfieldsSound	10.3390/rs14133036 doi (DE-627)DOAJ036855057 (DE-599)DOAJ3e0591b3aa09454399dc9b5543a6cd29 DE-627 ger DE-627 rakwb eng Zhe Chen verfasserin aut Prediction of Potential Geothermal Disaster Areas along the Yunnan–Tibet Railway Project 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As China’s railways continue to expand into the Qinghai–Tibet Plateau, the number of deep-buried long tunnels is increasing. Tunnel-damaging geothermal disasters have become a common problem in underground engineering. Predicting the potential geothermal disaster areas along the Yunnan–Tibet railway project is conducive to its planning and construction and the realization of the United Nations Sustainable Development Goals (SDGs)—specifically, the industry, innovation and infrastructure goal (SDG 9). In this paper, the Yunnan–Tibet railway project was the study area. Landsat-8 images and other spatial data were used to investigate causes and distributions of geothermal disasters. A collinearity diagnosis of environmental variables was carried out. Twelve environmental variables, such as land surface temperature, were selected to predict potential geothermal disaster areas using four niche models (MaxEnt, Bioclim, Domain and GARP). The prediction results were divided into four levels and had different characteristics. Among them, the area under receiver operating characteristic curve (AUC) and kappa values of the MaxEnt model were the highest, at 0.84 and 0.63, respectively. Its prediction accuracy was the highest and the algorithm results are more suitable for the prediction of geothermal disasters. The prediction results show that the geothermal disaster potential is greatest in the Markam-Deqen, Zuogong-Zayu and Baxoi-Zayu regions. Through jack-knife analysis, it was found that the land surface temperature, active faults, water system distribution and Moho depth are the key environmental predictors of potential geothermal disaster areas. The research results provide a reference for the design and construction of the Yunnan–Tibet railway project and associated sustainable development. Yunnan–Tibet railway geothermal disaster sustainable development Landsat-8 niche model Science Q Ruichun Chang verfasserin aut Huadong Guo verfasserin aut Xiangjun Pei verfasserin aut Wenbo Zhao verfasserin aut Zhengbo Yu verfasserin aut Lu Zou verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 13, p 3036 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:13, p 3036 https://doi.org/10.3390/rs14133036 kostenfrei https://doaj.org/article/3e0591b3aa09454399dc9b5543a6cd29 kostenfrei https://www.mdpi.com/2072-4292/14/13/3036 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 13, p 3036
language	English
source	In Remote Sensing 14(2022), 13, p 3036 volume:14 year:2022 number:13, p 3036
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author	Zhe Chen
spellingShingle	Zhe Chen misc Yunnan–Tibet railway misc geothermal disaster misc sustainable development misc Landsat-8 misc niche model misc Science misc Q Prediction of Potential Geothermal Disaster Areas along the Yunnan–Tibet Railway Project
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topic_title	Prediction of Potential Geothermal Disaster Areas along the Yunnan–Tibet Railway Project Yunnan–Tibet railway geothermal disaster sustainable development Landsat-8 niche model
topic	misc Yunnan–Tibet railway misc geothermal disaster misc sustainable development misc Landsat-8 misc niche model misc Science misc Q
topic_unstemmed	misc Yunnan–Tibet railway misc geothermal disaster misc sustainable development misc Landsat-8 misc niche model misc Science misc Q
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title	Prediction of Potential Geothermal Disaster Areas along the Yunnan–Tibet Railway Project
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title_sort	prediction of potential geothermal disaster areas along the yunnan–tibet railway project
title_auth	Prediction of Potential Geothermal Disaster Areas along the Yunnan–Tibet Railway Project
abstract	As China’s railways continue to expand into the Qinghai–Tibet Plateau, the number of deep-buried long tunnels is increasing. Tunnel-damaging geothermal disasters have become a common problem in underground engineering. Predicting the potential geothermal disaster areas along the Yunnan–Tibet railway project is conducive to its planning and construction and the realization of the United Nations Sustainable Development Goals (SDGs)—specifically, the industry, innovation and infrastructure goal (SDG 9). In this paper, the Yunnan–Tibet railway project was the study area. Landsat-8 images and other spatial data were used to investigate causes and distributions of geothermal disasters. A collinearity diagnosis of environmental variables was carried out. Twelve environmental variables, such as land surface temperature, were selected to predict potential geothermal disaster areas using four niche models (MaxEnt, Bioclim, Domain and GARP). The prediction results were divided into four levels and had different characteristics. Among them, the area under receiver operating characteristic curve (AUC) and kappa values of the MaxEnt model were the highest, at 0.84 and 0.63, respectively. Its prediction accuracy was the highest and the algorithm results are more suitable for the prediction of geothermal disasters. The prediction results show that the geothermal disaster potential is greatest in the Markam-Deqen, Zuogong-Zayu and Baxoi-Zayu regions. Through jack-knife analysis, it was found that the land surface temperature, active faults, water system distribution and Moho depth are the key environmental predictors of potential geothermal disaster areas. The research results provide a reference for the design and construction of the Yunnan–Tibet railway project and associated sustainable development.
abstractGer	As China’s railways continue to expand into the Qinghai–Tibet Plateau, the number of deep-buried long tunnels is increasing. Tunnel-damaging geothermal disasters have become a common problem in underground engineering. Predicting the potential geothermal disaster areas along the Yunnan–Tibet railway project is conducive to its planning and construction and the realization of the United Nations Sustainable Development Goals (SDGs)—specifically, the industry, innovation and infrastructure goal (SDG 9). In this paper, the Yunnan–Tibet railway project was the study area. Landsat-8 images and other spatial data were used to investigate causes and distributions of geothermal disasters. A collinearity diagnosis of environmental variables was carried out. Twelve environmental variables, such as land surface temperature, were selected to predict potential geothermal disaster areas using four niche models (MaxEnt, Bioclim, Domain and GARP). The prediction results were divided into four levels and had different characteristics. Among them, the area under receiver operating characteristic curve (AUC) and kappa values of the MaxEnt model were the highest, at 0.84 and 0.63, respectively. Its prediction accuracy was the highest and the algorithm results are more suitable for the prediction of geothermal disasters. The prediction results show that the geothermal disaster potential is greatest in the Markam-Deqen, Zuogong-Zayu and Baxoi-Zayu regions. Through jack-knife analysis, it was found that the land surface temperature, active faults, water system distribution and Moho depth are the key environmental predictors of potential geothermal disaster areas. The research results provide a reference for the design and construction of the Yunnan–Tibet railway project and associated sustainable development.
abstract_unstemmed	As China’s railways continue to expand into the Qinghai–Tibet Plateau, the number of deep-buried long tunnels is increasing. Tunnel-damaging geothermal disasters have become a common problem in underground engineering. Predicting the potential geothermal disaster areas along the Yunnan–Tibet railway project is conducive to its planning and construction and the realization of the United Nations Sustainable Development Goals (SDGs)—specifically, the industry, innovation and infrastructure goal (SDG 9). In this paper, the Yunnan–Tibet railway project was the study area. Landsat-8 images and other spatial data were used to investigate causes and distributions of geothermal disasters. A collinearity diagnosis of environmental variables was carried out. Twelve environmental variables, such as land surface temperature, were selected to predict potential geothermal disaster areas using four niche models (MaxEnt, Bioclim, Domain and GARP). The prediction results were divided into four levels and had different characteristics. Among them, the area under receiver operating characteristic curve (AUC) and kappa values of the MaxEnt model were the highest, at 0.84 and 0.63, respectively. Its prediction accuracy was the highest and the algorithm results are more suitable for the prediction of geothermal disasters. The prediction results show that the geothermal disaster potential is greatest in the Markam-Deqen, Zuogong-Zayu and Baxoi-Zayu regions. Through jack-knife analysis, it was found that the land surface temperature, active faults, water system distribution and Moho depth are the key environmental predictors of potential geothermal disaster areas. The research results provide a reference for the design and construction of the Yunnan–Tibet railway project and associated sustainable development.
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title_short	Prediction of Potential Geothermal Disaster Areas along the Yunnan–Tibet Railway Project
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Tunnel-damaging geothermal disasters have become a common problem in underground engineering. Predicting the potential geothermal disaster areas along the Yunnan–Tibet railway project is conducive to its planning and construction and the realization of the United Nations Sustainable Development Goals (SDGs)—specifically, the industry, innovation and infrastructure goal (SDG 9). In this paper, the Yunnan–Tibet railway project was the study area. Landsat-8 images and other spatial data were used to investigate causes and distributions of geothermal disasters. A collinearity diagnosis of environmental variables was carried out. Twelve environmental variables, such as land surface temperature, were selected to predict potential geothermal disaster areas using four niche models (MaxEnt, Bioclim, Domain and GARP). The prediction results were divided into four levels and had different characteristics. Among them, the area under receiver operating characteristic curve (AUC) and kappa values of the MaxEnt model were the highest, at 0.84 and 0.63, respectively. Its prediction accuracy was the highest and the algorithm results are more suitable for the prediction of geothermal disasters. The prediction results show that the geothermal disaster potential is greatest in the Markam-Deqen, Zuogong-Zayu and Baxoi-Zayu regions. Through jack-knife analysis, it was found that the land surface temperature, active faults, water system distribution and Moho depth are the key environmental predictors of potential geothermal disaster areas. 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Prediction of Potential Geothermal Disaster Areas along the Yunnan–Tibet Railway Project

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