Integrating multi-source data to assess land subsidence sensitivity and management policies

Uneven land subsidence will cause damage to urban buildings and infrastructure and pose risks to human production and life. This study proposes a new methodology for factor identification and prediction of urban land subsidence that integrates physics-driven models and data-driven models. The drivin...
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Autor*in:	Yang, Xiao [verfasserIn] Jia, Chao [verfasserIn] Sun, Hao [verfasserIn] Yang, Tian [verfasserIn] Yao, Yue [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Geological hazard prediction Land subsidence Multi-source data Machine learning Identification system

Übergeordnetes Werk:	Enthalten in: Environmental impact assessment review - Amsterdam [u.a.] : Elsevier Science, 1980, 104
Übergeordnetes Werk:	volume:104

DOI / URN:	10.1016/j.eiar.2023.107315

Katalog-ID:	ELV06594691X

Internformat


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245	1	0	\|a Integrating multi-source data to assess land subsidence sensitivity and management policies
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520			\|a Uneven land subsidence will cause damage to urban buildings and infrastructure and pose risks to human production and life. This study proposes a new methodology for factor identification and prediction of urban land subsidence that integrates physics-driven models and data-driven models. The driving factor layer was generated using multi-source datasets, and a comprehensive factor identification system was implemented. The machine learning method was used to calculate the comprehensive weight of each driving factor, and the identification and evaluation model of the land subsidence factor in Heze City was established. Then, the SARIMA model and the MODFLOW model were used to predict the development trend of the main influencing factors of land subsidence. Finally, the development trend of land subsidence was evaluated based on the physics-based machine learning model. The results show that the maximum average annual subsidence rate from 2016 to 2020 deduced by InSAR is 130.22 mm/yr, and the subsidence area accounts for 85.09%. It is estimated that the maximum annual average rate of land subsidence will be 65.14 mm/yr by 2025, and the subsidence area will account for 92.56%. The development degree of land subsidence has a significant relationship with groundwater exploitation and coal mining planning. By 2025, the proportion of areas with unchanged sensitivity levels will be 61.39%, and the prevention and control plan can be effectively implemented in most areas. The research provides a new methodology for evaluating the effectiveness of government planning policies and promoting scientific prevention and control.
650		4	\|a Geological hazard prediction
650		4	\|a Land subsidence
650		4	\|a Multi-source data
650		4	\|a Machine learning
650		4	\|a Identification system
700	1		\|a Jia, Chao \|e verfasserin \|4 aut
700	1		\|a Sun, Hao \|e verfasserin \|4 aut
700	1		\|a Yang, Tian \|e verfasserin \|4 aut
700	1		\|a Yao, Yue \|e verfasserin \|4 aut
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publishDate	2023
allfields	10.1016/j.eiar.2023.107315 doi (DE-627)ELV06594691X (ELSEVIER)S0195-9255(23)00281-0 DE-627 ger DE-627 rda eng 333.7 VZ INTRECHT DE-1a fid 30.00 bkl 43.00 bkl Yang, Xiao verfasserin aut Integrating multi-source data to assess land subsidence sensitivity and management policies 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uneven land subsidence will cause damage to urban buildings and infrastructure and pose risks to human production and life. This study proposes a new methodology for factor identification and prediction of urban land subsidence that integrates physics-driven models and data-driven models. The driving factor layer was generated using multi-source datasets, and a comprehensive factor identification system was implemented. The machine learning method was used to calculate the comprehensive weight of each driving factor, and the identification and evaluation model of the land subsidence factor in Heze City was established. Then, the SARIMA model and the MODFLOW model were used to predict the development trend of the main influencing factors of land subsidence. Finally, the development trend of land subsidence was evaluated based on the physics-based machine learning model. The results show that the maximum average annual subsidence rate from 2016 to 2020 deduced by InSAR is 130.22 mm/yr, and the subsidence area accounts for 85.09%. It is estimated that the maximum annual average rate of land subsidence will be 65.14 mm/yr by 2025, and the subsidence area will account for 92.56%. The development degree of land subsidence has a significant relationship with groundwater exploitation and coal mining planning. By 2025, the proportion of areas with unchanged sensitivity levels will be 61.39%, and the prevention and control plan can be effectively implemented in most areas. The research provides a new methodology for evaluating the effectiveness of government planning policies and promoting scientific prevention and control. Geological hazard prediction Land subsidence Multi-source data Machine learning Identification system Jia, Chao verfasserin aut Sun, Hao verfasserin aut Yang, Tian verfasserin aut Yao, Yue verfasserin aut Enthalten in Environmental impact assessment review Amsterdam [u.a.] : Elsevier Science, 1980 104 Online-Ressource (DE-627)320604209 (DE-600)2020543-0 (DE-576)259271748 0195-9255 nnns volume:104 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-INTRECHT SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.00 Naturwissenschaften allgemein: Allgemeines VZ 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 104
spelling	10.1016/j.eiar.2023.107315 doi (DE-627)ELV06594691X (ELSEVIER)S0195-9255(23)00281-0 DE-627 ger DE-627 rda eng 333.7 VZ INTRECHT DE-1a fid 30.00 bkl 43.00 bkl Yang, Xiao verfasserin aut Integrating multi-source data to assess land subsidence sensitivity and management policies 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uneven land subsidence will cause damage to urban buildings and infrastructure and pose risks to human production and life. This study proposes a new methodology for factor identification and prediction of urban land subsidence that integrates physics-driven models and data-driven models. The driving factor layer was generated using multi-source datasets, and a comprehensive factor identification system was implemented. The machine learning method was used to calculate the comprehensive weight of each driving factor, and the identification and evaluation model of the land subsidence factor in Heze City was established. Then, the SARIMA model and the MODFLOW model were used to predict the development trend of the main influencing factors of land subsidence. Finally, the development trend of land subsidence was evaluated based on the physics-based machine learning model. The results show that the maximum average annual subsidence rate from 2016 to 2020 deduced by InSAR is 130.22 mm/yr, and the subsidence area accounts for 85.09%. It is estimated that the maximum annual average rate of land subsidence will be 65.14 mm/yr by 2025, and the subsidence area will account for 92.56%. The development degree of land subsidence has a significant relationship with groundwater exploitation and coal mining planning. By 2025, the proportion of areas with unchanged sensitivity levels will be 61.39%, and the prevention and control plan can be effectively implemented in most areas. The research provides a new methodology for evaluating the effectiveness of government planning policies and promoting scientific prevention and control. Geological hazard prediction Land subsidence Multi-source data Machine learning Identification system Jia, Chao verfasserin aut Sun, Hao verfasserin aut Yang, Tian verfasserin aut Yao, Yue verfasserin aut Enthalten in Environmental impact assessment review Amsterdam [u.a.] : Elsevier Science, 1980 104 Online-Ressource (DE-627)320604209 (DE-600)2020543-0 (DE-576)259271748 0195-9255 nnns volume:104 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-INTRECHT SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.00 Naturwissenschaften allgemein: Allgemeines VZ 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 104
allfields_unstemmed	10.1016/j.eiar.2023.107315 doi (DE-627)ELV06594691X (ELSEVIER)S0195-9255(23)00281-0 DE-627 ger DE-627 rda eng 333.7 VZ INTRECHT DE-1a fid 30.00 bkl 43.00 bkl Yang, Xiao verfasserin aut Integrating multi-source data to assess land subsidence sensitivity and management policies 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uneven land subsidence will cause damage to urban buildings and infrastructure and pose risks to human production and life. This study proposes a new methodology for factor identification and prediction of urban land subsidence that integrates physics-driven models and data-driven models. The driving factor layer was generated using multi-source datasets, and a comprehensive factor identification system was implemented. The machine learning method was used to calculate the comprehensive weight of each driving factor, and the identification and evaluation model of the land subsidence factor in Heze City was established. Then, the SARIMA model and the MODFLOW model were used to predict the development trend of the main influencing factors of land subsidence. Finally, the development trend of land subsidence was evaluated based on the physics-based machine learning model. The results show that the maximum average annual subsidence rate from 2016 to 2020 deduced by InSAR is 130.22 mm/yr, and the subsidence area accounts for 85.09%. It is estimated that the maximum annual average rate of land subsidence will be 65.14 mm/yr by 2025, and the subsidence area will account for 92.56%. The development degree of land subsidence has a significant relationship with groundwater exploitation and coal mining planning. By 2025, the proportion of areas with unchanged sensitivity levels will be 61.39%, and the prevention and control plan can be effectively implemented in most areas. The research provides a new methodology for evaluating the effectiveness of government planning policies and promoting scientific prevention and control. Geological hazard prediction Land subsidence Multi-source data Machine learning Identification system Jia, Chao verfasserin aut Sun, Hao verfasserin aut Yang, Tian verfasserin aut Yao, Yue verfasserin aut Enthalten in Environmental impact assessment review Amsterdam [u.a.] : Elsevier Science, 1980 104 Online-Ressource (DE-627)320604209 (DE-600)2020543-0 (DE-576)259271748 0195-9255 nnns volume:104 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-INTRECHT SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.00 Naturwissenschaften allgemein: Allgemeines VZ 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 104
allfieldsGer	10.1016/j.eiar.2023.107315 doi (DE-627)ELV06594691X (ELSEVIER)S0195-9255(23)00281-0 DE-627 ger DE-627 rda eng 333.7 VZ INTRECHT DE-1a fid 30.00 bkl 43.00 bkl Yang, Xiao verfasserin aut Integrating multi-source data to assess land subsidence sensitivity and management policies 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uneven land subsidence will cause damage to urban buildings and infrastructure and pose risks to human production and life. This study proposes a new methodology for factor identification and prediction of urban land subsidence that integrates physics-driven models and data-driven models. The driving factor layer was generated using multi-source datasets, and a comprehensive factor identification system was implemented. The machine learning method was used to calculate the comprehensive weight of each driving factor, and the identification and evaluation model of the land subsidence factor in Heze City was established. Then, the SARIMA model and the MODFLOW model were used to predict the development trend of the main influencing factors of land subsidence. Finally, the development trend of land subsidence was evaluated based on the physics-based machine learning model. The results show that the maximum average annual subsidence rate from 2016 to 2020 deduced by InSAR is 130.22 mm/yr, and the subsidence area accounts for 85.09%. It is estimated that the maximum annual average rate of land subsidence will be 65.14 mm/yr by 2025, and the subsidence area will account for 92.56%. The development degree of land subsidence has a significant relationship with groundwater exploitation and coal mining planning. By 2025, the proportion of areas with unchanged sensitivity levels will be 61.39%, and the prevention and control plan can be effectively implemented in most areas. The research provides a new methodology for evaluating the effectiveness of government planning policies and promoting scientific prevention and control. Geological hazard prediction Land subsidence Multi-source data Machine learning Identification system Jia, Chao verfasserin aut Sun, Hao verfasserin aut Yang, Tian verfasserin aut Yao, Yue verfasserin aut Enthalten in Environmental impact assessment review Amsterdam [u.a.] : Elsevier Science, 1980 104 Online-Ressource (DE-627)320604209 (DE-600)2020543-0 (DE-576)259271748 0195-9255 nnns volume:104 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-INTRECHT SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.00 Naturwissenschaften allgemein: Allgemeines VZ 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 104
allfieldsSound	10.1016/j.eiar.2023.107315 doi (DE-627)ELV06594691X (ELSEVIER)S0195-9255(23)00281-0 DE-627 ger DE-627 rda eng 333.7 VZ INTRECHT DE-1a fid 30.00 bkl 43.00 bkl Yang, Xiao verfasserin aut Integrating multi-source data to assess land subsidence sensitivity and management policies 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Uneven land subsidence will cause damage to urban buildings and infrastructure and pose risks to human production and life. This study proposes a new methodology for factor identification and prediction of urban land subsidence that integrates physics-driven models and data-driven models. The driving factor layer was generated using multi-source datasets, and a comprehensive factor identification system was implemented. The machine learning method was used to calculate the comprehensive weight of each driving factor, and the identification and evaluation model of the land subsidence factor in Heze City was established. Then, the SARIMA model and the MODFLOW model were used to predict the development trend of the main influencing factors of land subsidence. Finally, the development trend of land subsidence was evaluated based on the physics-based machine learning model. The results show that the maximum average annual subsidence rate from 2016 to 2020 deduced by InSAR is 130.22 mm/yr, and the subsidence area accounts for 85.09%. It is estimated that the maximum annual average rate of land subsidence will be 65.14 mm/yr by 2025, and the subsidence area will account for 92.56%. The development degree of land subsidence has a significant relationship with groundwater exploitation and coal mining planning. By 2025, the proportion of areas with unchanged sensitivity levels will be 61.39%, and the prevention and control plan can be effectively implemented in most areas. The research provides a new methodology for evaluating the effectiveness of government planning policies and promoting scientific prevention and control. Geological hazard prediction Land subsidence Multi-source data Machine learning Identification system Jia, Chao verfasserin aut Sun, Hao verfasserin aut Yang, Tian verfasserin aut Yao, Yue verfasserin aut Enthalten in Environmental impact assessment review Amsterdam [u.a.] : Elsevier Science, 1980 104 Online-Ressource (DE-627)320604209 (DE-600)2020543-0 (DE-576)259271748 0195-9255 nnns volume:104 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-INTRECHT SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 30.00 Naturwissenschaften allgemein: Allgemeines VZ 43.00 Umweltforschung Umweltschutz: Allgemeines VZ AR 104
language	English
source	Enthalten in Environmental impact assessment review 104 volume:104
sourceStr	Enthalten in Environmental impact assessment review 104 volume:104
format_phy_str_mv	Article
bklname	Naturwissenschaften allgemein: Allgemeines Umweltforschung Umweltschutz: Allgemeines
institution	findex.gbv.de
topic_facet	Geological hazard prediction Land subsidence Multi-source data Machine learning Identification system
dewey-raw	333.7
isfreeaccess_bool	false
container_title	Environmental impact assessment review
authorswithroles_txt_mv	Yang, Xiao @@aut@@ Jia, Chao @@aut@@ Sun, Hao @@aut@@ Yang, Tian @@aut@@ Yao, Yue @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320604209
dewey-sort	3333.7
id	ELV06594691X
language_de	englisch
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author	Yang, Xiao
spellingShingle	Yang, Xiao ddc 333.7 fid INTRECHT bkl 30.00 bkl 43.00 misc Geological hazard prediction misc Land subsidence misc Multi-source data misc Machine learning misc Identification system Integrating multi-source data to assess land subsidence sensitivity and management policies
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topic_title	333.7 VZ INTRECHT DE-1a fid 30.00 bkl 43.00 bkl Integrating multi-source data to assess land subsidence sensitivity and management policies Geological hazard prediction Land subsidence Multi-source data Machine learning Identification system
topic	ddc 333.7 fid INTRECHT bkl 30.00 bkl 43.00 misc Geological hazard prediction misc Land subsidence misc Multi-source data misc Machine learning misc Identification system
topic_unstemmed	ddc 333.7 fid INTRECHT bkl 30.00 bkl 43.00 misc Geological hazard prediction misc Land subsidence misc Multi-source data misc Machine learning misc Identification system
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title	Integrating multi-source data to assess land subsidence sensitivity and management policies
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title_full	Integrating multi-source data to assess land subsidence sensitivity and management policies
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author_browse	Yang, Xiao Jia, Chao Sun, Hao Yang, Tian Yao, Yue
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author-letter	Yang, Xiao
doi_str_mv	10.1016/j.eiar.2023.107315
dewey-full	333.7
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title_sort	integrating multi-source data to assess land subsidence sensitivity and management policies
title_auth	Integrating multi-source data to assess land subsidence sensitivity and management policies
abstract	Uneven land subsidence will cause damage to urban buildings and infrastructure and pose risks to human production and life. This study proposes a new methodology for factor identification and prediction of urban land subsidence that integrates physics-driven models and data-driven models. The driving factor layer was generated using multi-source datasets, and a comprehensive factor identification system was implemented. The machine learning method was used to calculate the comprehensive weight of each driving factor, and the identification and evaluation model of the land subsidence factor in Heze City was established. Then, the SARIMA model and the MODFLOW model were used to predict the development trend of the main influencing factors of land subsidence. Finally, the development trend of land subsidence was evaluated based on the physics-based machine learning model. The results show that the maximum average annual subsidence rate from 2016 to 2020 deduced by InSAR is 130.22 mm/yr, and the subsidence area accounts for 85.09%. It is estimated that the maximum annual average rate of land subsidence will be 65.14 mm/yr by 2025, and the subsidence area will account for 92.56%. The development degree of land subsidence has a significant relationship with groundwater exploitation and coal mining planning. By 2025, the proportion of areas with unchanged sensitivity levels will be 61.39%, and the prevention and control plan can be effectively implemented in most areas. The research provides a new methodology for evaluating the effectiveness of government planning policies and promoting scientific prevention and control.
abstractGer	Uneven land subsidence will cause damage to urban buildings and infrastructure and pose risks to human production and life. This study proposes a new methodology for factor identification and prediction of urban land subsidence that integrates physics-driven models and data-driven models. The driving factor layer was generated using multi-source datasets, and a comprehensive factor identification system was implemented. The machine learning method was used to calculate the comprehensive weight of each driving factor, and the identification and evaluation model of the land subsidence factor in Heze City was established. Then, the SARIMA model and the MODFLOW model were used to predict the development trend of the main influencing factors of land subsidence. Finally, the development trend of land subsidence was evaluated based on the physics-based machine learning model. The results show that the maximum average annual subsidence rate from 2016 to 2020 deduced by InSAR is 130.22 mm/yr, and the subsidence area accounts for 85.09%. It is estimated that the maximum annual average rate of land subsidence will be 65.14 mm/yr by 2025, and the subsidence area will account for 92.56%. The development degree of land subsidence has a significant relationship with groundwater exploitation and coal mining planning. By 2025, the proportion of areas with unchanged sensitivity levels will be 61.39%, and the prevention and control plan can be effectively implemented in most areas. The research provides a new methodology for evaluating the effectiveness of government planning policies and promoting scientific prevention and control.
abstract_unstemmed	Uneven land subsidence will cause damage to urban buildings and infrastructure and pose risks to human production and life. This study proposes a new methodology for factor identification and prediction of urban land subsidence that integrates physics-driven models and data-driven models. The driving factor layer was generated using multi-source datasets, and a comprehensive factor identification system was implemented. The machine learning method was used to calculate the comprehensive weight of each driving factor, and the identification and evaluation model of the land subsidence factor in Heze City was established. Then, the SARIMA model and the MODFLOW model were used to predict the development trend of the main influencing factors of land subsidence. Finally, the development trend of land subsidence was evaluated based on the physics-based machine learning model. The results show that the maximum average annual subsidence rate from 2016 to 2020 deduced by InSAR is 130.22 mm/yr, and the subsidence area accounts for 85.09%. It is estimated that the maximum annual average rate of land subsidence will be 65.14 mm/yr by 2025, and the subsidence area will account for 92.56%. The development degree of land subsidence has a significant relationship with groundwater exploitation and coal mining planning. By 2025, the proportion of areas with unchanged sensitivity levels will be 61.39%, and the prevention and control plan can be effectively implemented in most areas. The research provides a new methodology for evaluating the effectiveness of government planning policies and promoting scientific prevention and control.
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title_short	Integrating multi-source data to assess land subsidence sensitivity and management policies
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author2	Jia, Chao Sun, Hao Yang, Tian Yao, Yue
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up_date	2024-07-07T00:47:56.881Z
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score	7.39983

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Integrating multi-source data to assess land subsidence sensitivity and management policies

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