Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam

Abstract This study proposes an automatic identification approach for landslide dam formation using a real-time abnormal hydrology record and artificial neural network (ANN). In Taiwan, natural dams occur frequently and have the potential to cause significant disasters, especially in the presence of...
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Autor*in:	Lee, S.-P. [verfasserIn] Chen, Y.-C. [verfasserIn] Shieh, C.-L. [verfasserIn] Kuo, Y.-S. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Natural dam Artificial neural network Dam failure

Übergeordnetes Werk:	Enthalten in: Landslides - Berlin : Springer, 2004, 11(2013), 6 vom: 01. Nov., Seite 1007-1017
Übergeordnetes Werk:	volume:11 ; year:2013 ; number:6 ; day:01 ; month:11 ; pages:1007-1017

Links:	Volltext

DOI / URN:	10.1007/s10346-013-0441-1

Katalog-ID:	SPR009774092

Internformat


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520			\|a Abstract This study proposes an automatic identification approach for landslide dam formation using a real-time abnormal hydrology record and artificial neural network (ANN). In Taiwan, natural dams occur frequently and have the potential to cause significant disasters, especially in the presence of fragile geological conditions such as steep mountains, earthquakes, intense typhoon, and storm events. During a river blockage event, obtaining immediate real-time information can be difficult or even impossible due to extreme weather conditions, remote locations, and the mountainous terrain of the area. As a result of these issues, there is a strong need for a practical tool that can not only detect the occurrence of this type of natural dam but also estimate the potential real-time risk of dam failure. In order to develop the appropriate safety response, a rapid preliminary assessment of the failure type must be identified, followed by a thorough investigation into the failure process after the event has occurred. In this study, a new method is developed that applies an ANN model (1) to evaluate, in real time, the occurrence of a natural dam and the storage changes behind that dam; (2) to rapidly assess the natural dam failure type and failure process; and (3) to reconstruct the dam height variation during the dam failure process after the event. The proposed methodology is applied to Chi-Shan River Basin located in Kaohsiung, Taiwan, during Typhoon Morakot in 2009. In 3 days, Typhoon Morakot produced 1,911 mm of precipitation. The typhoon event produced a landslide dam and a debris flow that inundated Hsiaolin Village, causing 398 deaths. The tragic results of this case study demonstrate the need for an applicable, accurate, and efficient method to evaluate dam formation and its failure rate.
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650		4	\|a Artificial neural network \|7 (dpeaa)DE-He213
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700	1		\|a Chen, Y.-C. \|e verfasserin \|4 aut
700	1		\|a Shieh, C.-L. \|e verfasserin \|4 aut
700	1		\|a Kuo, Y.-S. \|e verfasserin \|4 aut
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912			\|a GBV_ILN_2048
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912			\|a GBV_ILN_2057
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912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
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951			\|a AR
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Indexfelder

author_variant	s p l spl y c c ycc c l s cls y s k ysk
matchkey_str	article:16125118:2013----::snratmanrahdooybevtosodniyrvrlcaev
hierarchy_sort_str	2013
bklnumber	38.42 38.58 56.20
publishDate	2013
allfields	10.1007/s10346-013-0441-1 doi (DE-627)SPR009774092 (SPR)s10346-013-0441-1-e DE-627 ger DE-627 rakwb eng 550 ASE 550 ASE 38.42 bkl 38.58 bkl 56.20 bkl Lee, S.-P. verfasserin aut Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study proposes an automatic identification approach for landslide dam formation using a real-time abnormal hydrology record and artificial neural network (ANN). In Taiwan, natural dams occur frequently and have the potential to cause significant disasters, especially in the presence of fragile geological conditions such as steep mountains, earthquakes, intense typhoon, and storm events. During a river blockage event, obtaining immediate real-time information can be difficult or even impossible due to extreme weather conditions, remote locations, and the mountainous terrain of the area. As a result of these issues, there is a strong need for a practical tool that can not only detect the occurrence of this type of natural dam but also estimate the potential real-time risk of dam failure. In order to develop the appropriate safety response, a rapid preliminary assessment of the failure type must be identified, followed by a thorough investigation into the failure process after the event has occurred. In this study, a new method is developed that applies an ANN model (1) to evaluate, in real time, the occurrence of a natural dam and the storage changes behind that dam; (2) to rapidly assess the natural dam failure type and failure process; and (3) to reconstruct the dam height variation during the dam failure process after the event. The proposed methodology is applied to Chi-Shan River Basin located in Kaohsiung, Taiwan, during Typhoon Morakot in 2009. In 3 days, Typhoon Morakot produced 1,911 mm of precipitation. The typhoon event produced a landslide dam and a debris flow that inundated Hsiaolin Village, causing 398 deaths. The tragic results of this case study demonstrate the need for an applicable, accurate, and efficient method to evaluate dam formation and its failure rate. Natural dam (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Dam failure (dpeaa)DE-He213 Chen, Y.-C. verfasserin aut Shieh, C.-L. verfasserin aut Kuo, Y.-S. verfasserin aut Enthalten in Landslides Berlin : Springer, 2004 11(2013), 6 vom: 01. Nov., Seite 1007-1017 (DE-627)384045197 (DE-600)2141883-4 1612-5118 nnns volume:11 year:2013 number:6 day:01 month:11 pages:1007-1017 https://dx.doi.org/10.1007/s10346-013-0441-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.42 ASE 38.58 ASE 56.20 ASE AR 11 2013 6 01 11 1007-1017
spelling	10.1007/s10346-013-0441-1 doi (DE-627)SPR009774092 (SPR)s10346-013-0441-1-e DE-627 ger DE-627 rakwb eng 550 ASE 550 ASE 38.42 bkl 38.58 bkl 56.20 bkl Lee, S.-P. verfasserin aut Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study proposes an automatic identification approach for landslide dam formation using a real-time abnormal hydrology record and artificial neural network (ANN). In Taiwan, natural dams occur frequently and have the potential to cause significant disasters, especially in the presence of fragile geological conditions such as steep mountains, earthquakes, intense typhoon, and storm events. During a river blockage event, obtaining immediate real-time information can be difficult or even impossible due to extreme weather conditions, remote locations, and the mountainous terrain of the area. As a result of these issues, there is a strong need for a practical tool that can not only detect the occurrence of this type of natural dam but also estimate the potential real-time risk of dam failure. In order to develop the appropriate safety response, a rapid preliminary assessment of the failure type must be identified, followed by a thorough investigation into the failure process after the event has occurred. In this study, a new method is developed that applies an ANN model (1) to evaluate, in real time, the occurrence of a natural dam and the storage changes behind that dam; (2) to rapidly assess the natural dam failure type and failure process; and (3) to reconstruct the dam height variation during the dam failure process after the event. The proposed methodology is applied to Chi-Shan River Basin located in Kaohsiung, Taiwan, during Typhoon Morakot in 2009. In 3 days, Typhoon Morakot produced 1,911 mm of precipitation. The typhoon event produced a landslide dam and a debris flow that inundated Hsiaolin Village, causing 398 deaths. The tragic results of this case study demonstrate the need for an applicable, accurate, and efficient method to evaluate dam formation and its failure rate. Natural dam (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Dam failure (dpeaa)DE-He213 Chen, Y.-C. verfasserin aut Shieh, C.-L. verfasserin aut Kuo, Y.-S. verfasserin aut Enthalten in Landslides Berlin : Springer, 2004 11(2013), 6 vom: 01. Nov., Seite 1007-1017 (DE-627)384045197 (DE-600)2141883-4 1612-5118 nnns volume:11 year:2013 number:6 day:01 month:11 pages:1007-1017 https://dx.doi.org/10.1007/s10346-013-0441-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.42 ASE 38.58 ASE 56.20 ASE AR 11 2013 6 01 11 1007-1017
allfields_unstemmed	10.1007/s10346-013-0441-1 doi (DE-627)SPR009774092 (SPR)s10346-013-0441-1-e DE-627 ger DE-627 rakwb eng 550 ASE 550 ASE 38.42 bkl 38.58 bkl 56.20 bkl Lee, S.-P. verfasserin aut Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study proposes an automatic identification approach for landslide dam formation using a real-time abnormal hydrology record and artificial neural network (ANN). In Taiwan, natural dams occur frequently and have the potential to cause significant disasters, especially in the presence of fragile geological conditions such as steep mountains, earthquakes, intense typhoon, and storm events. During a river blockage event, obtaining immediate real-time information can be difficult or even impossible due to extreme weather conditions, remote locations, and the mountainous terrain of the area. As a result of these issues, there is a strong need for a practical tool that can not only detect the occurrence of this type of natural dam but also estimate the potential real-time risk of dam failure. In order to develop the appropriate safety response, a rapid preliminary assessment of the failure type must be identified, followed by a thorough investigation into the failure process after the event has occurred. In this study, a new method is developed that applies an ANN model (1) to evaluate, in real time, the occurrence of a natural dam and the storage changes behind that dam; (2) to rapidly assess the natural dam failure type and failure process; and (3) to reconstruct the dam height variation during the dam failure process after the event. The proposed methodology is applied to Chi-Shan River Basin located in Kaohsiung, Taiwan, during Typhoon Morakot in 2009. In 3 days, Typhoon Morakot produced 1,911 mm of precipitation. The typhoon event produced a landslide dam and a debris flow that inundated Hsiaolin Village, causing 398 deaths. The tragic results of this case study demonstrate the need for an applicable, accurate, and efficient method to evaluate dam formation and its failure rate. Natural dam (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Dam failure (dpeaa)DE-He213 Chen, Y.-C. verfasserin aut Shieh, C.-L. verfasserin aut Kuo, Y.-S. verfasserin aut Enthalten in Landslides Berlin : Springer, 2004 11(2013), 6 vom: 01. Nov., Seite 1007-1017 (DE-627)384045197 (DE-600)2141883-4 1612-5118 nnns volume:11 year:2013 number:6 day:01 month:11 pages:1007-1017 https://dx.doi.org/10.1007/s10346-013-0441-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.42 ASE 38.58 ASE 56.20 ASE AR 11 2013 6 01 11 1007-1017
allfieldsGer	10.1007/s10346-013-0441-1 doi (DE-627)SPR009774092 (SPR)s10346-013-0441-1-e DE-627 ger DE-627 rakwb eng 550 ASE 550 ASE 38.42 bkl 38.58 bkl 56.20 bkl Lee, S.-P. verfasserin aut Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study proposes an automatic identification approach for landslide dam formation using a real-time abnormal hydrology record and artificial neural network (ANN). In Taiwan, natural dams occur frequently and have the potential to cause significant disasters, especially in the presence of fragile geological conditions such as steep mountains, earthquakes, intense typhoon, and storm events. During a river blockage event, obtaining immediate real-time information can be difficult or even impossible due to extreme weather conditions, remote locations, and the mountainous terrain of the area. As a result of these issues, there is a strong need for a practical tool that can not only detect the occurrence of this type of natural dam but also estimate the potential real-time risk of dam failure. In order to develop the appropriate safety response, a rapid preliminary assessment of the failure type must be identified, followed by a thorough investigation into the failure process after the event has occurred. In this study, a new method is developed that applies an ANN model (1) to evaluate, in real time, the occurrence of a natural dam and the storage changes behind that dam; (2) to rapidly assess the natural dam failure type and failure process; and (3) to reconstruct the dam height variation during the dam failure process after the event. The proposed methodology is applied to Chi-Shan River Basin located in Kaohsiung, Taiwan, during Typhoon Morakot in 2009. In 3 days, Typhoon Morakot produced 1,911 mm of precipitation. The typhoon event produced a landslide dam and a debris flow that inundated Hsiaolin Village, causing 398 deaths. The tragic results of this case study demonstrate the need for an applicable, accurate, and efficient method to evaluate dam formation and its failure rate. Natural dam (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Dam failure (dpeaa)DE-He213 Chen, Y.-C. verfasserin aut Shieh, C.-L. verfasserin aut Kuo, Y.-S. verfasserin aut Enthalten in Landslides Berlin : Springer, 2004 11(2013), 6 vom: 01. Nov., Seite 1007-1017 (DE-627)384045197 (DE-600)2141883-4 1612-5118 nnns volume:11 year:2013 number:6 day:01 month:11 pages:1007-1017 https://dx.doi.org/10.1007/s10346-013-0441-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.42 ASE 38.58 ASE 56.20 ASE AR 11 2013 6 01 11 1007-1017
allfieldsSound	10.1007/s10346-013-0441-1 doi (DE-627)SPR009774092 (SPR)s10346-013-0441-1-e DE-627 ger DE-627 rakwb eng 550 ASE 550 ASE 38.42 bkl 38.58 bkl 56.20 bkl Lee, S.-P. verfasserin aut Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study proposes an automatic identification approach for landslide dam formation using a real-time abnormal hydrology record and artificial neural network (ANN). In Taiwan, natural dams occur frequently and have the potential to cause significant disasters, especially in the presence of fragile geological conditions such as steep mountains, earthquakes, intense typhoon, and storm events. During a river blockage event, obtaining immediate real-time information can be difficult or even impossible due to extreme weather conditions, remote locations, and the mountainous terrain of the area. As a result of these issues, there is a strong need for a practical tool that can not only detect the occurrence of this type of natural dam but also estimate the potential real-time risk of dam failure. In order to develop the appropriate safety response, a rapid preliminary assessment of the failure type must be identified, followed by a thorough investigation into the failure process after the event has occurred. In this study, a new method is developed that applies an ANN model (1) to evaluate, in real time, the occurrence of a natural dam and the storage changes behind that dam; (2) to rapidly assess the natural dam failure type and failure process; and (3) to reconstruct the dam height variation during the dam failure process after the event. The proposed methodology is applied to Chi-Shan River Basin located in Kaohsiung, Taiwan, during Typhoon Morakot in 2009. In 3 days, Typhoon Morakot produced 1,911 mm of precipitation. The typhoon event produced a landslide dam and a debris flow that inundated Hsiaolin Village, causing 398 deaths. The tragic results of this case study demonstrate the need for an applicable, accurate, and efficient method to evaluate dam formation and its failure rate. Natural dam (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Dam failure (dpeaa)DE-He213 Chen, Y.-C. verfasserin aut Shieh, C.-L. verfasserin aut Kuo, Y.-S. verfasserin aut Enthalten in Landslides Berlin : Springer, 2004 11(2013), 6 vom: 01. Nov., Seite 1007-1017 (DE-627)384045197 (DE-600)2141883-4 1612-5118 nnns volume:11 year:2013 number:6 day:01 month:11 pages:1007-1017 https://dx.doi.org/10.1007/s10346-013-0441-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.42 ASE 38.58 ASE 56.20 ASE AR 11 2013 6 01 11 1007-1017
language	English
source	Enthalten in Landslides 11(2013), 6 vom: 01. Nov., Seite 1007-1017 volume:11 year:2013 number:6 day:01 month:11 pages:1007-1017
sourceStr	Enthalten in Landslides 11(2013), 6 vom: 01. Nov., Seite 1007-1017 volume:11 year:2013 number:6 day:01 month:11 pages:1007-1017
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Natural dam Artificial neural network Dam failure
dewey-raw	550
isfreeaccess_bool	false
container_title	Landslides
authorswithroles_txt_mv	Lee, S.-P. @@aut@@ Chen, Y.-C. @@aut@@ Shieh, C.-L. @@aut@@ Kuo, Y.-S. @@aut@@
publishDateDaySort_date	2013-11-01T00:00:00Z
hierarchy_top_id	384045197
dewey-sort	3550
id	SPR009774092
language_de	englisch
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author	Lee, S.-P.
spellingShingle	Lee, S.-P. ddc 550 bkl 38.42 bkl 38.58 bkl 56.20 misc Natural dam misc Artificial neural network misc Dam failure Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam
authorStr	Lee, S.-P.
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topic_title	550 ASE 38.42 bkl 38.58 bkl 56.20 bkl Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam Natural dam (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Dam failure (dpeaa)DE-He213
topic	ddc 550 bkl 38.42 bkl 38.58 bkl 56.20 misc Natural dam misc Artificial neural network misc Dam failure
topic_unstemmed	ddc 550 bkl 38.42 bkl 38.58 bkl 56.20 misc Natural dam misc Artificial neural network misc Dam failure
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title	Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam
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title_full	Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam
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title_sort	using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam
title_auth	Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam
abstract	Abstract This study proposes an automatic identification approach for landslide dam formation using a real-time abnormal hydrology record and artificial neural network (ANN). In Taiwan, natural dams occur frequently and have the potential to cause significant disasters, especially in the presence of fragile geological conditions such as steep mountains, earthquakes, intense typhoon, and storm events. During a river blockage event, obtaining immediate real-time information can be difficult or even impossible due to extreme weather conditions, remote locations, and the mountainous terrain of the area. As a result of these issues, there is a strong need for a practical tool that can not only detect the occurrence of this type of natural dam but also estimate the potential real-time risk of dam failure. In order to develop the appropriate safety response, a rapid preliminary assessment of the failure type must be identified, followed by a thorough investigation into the failure process after the event has occurred. In this study, a new method is developed that applies an ANN model (1) to evaluate, in real time, the occurrence of a natural dam and the storage changes behind that dam; (2) to rapidly assess the natural dam failure type and failure process; and (3) to reconstruct the dam height variation during the dam failure process after the event. The proposed methodology is applied to Chi-Shan River Basin located in Kaohsiung, Taiwan, during Typhoon Morakot in 2009. In 3 days, Typhoon Morakot produced 1,911 mm of precipitation. The typhoon event produced a landslide dam and a debris flow that inundated Hsiaolin Village, causing 398 deaths. The tragic results of this case study demonstrate the need for an applicable, accurate, and efficient method to evaluate dam formation and its failure rate.
abstractGer	Abstract This study proposes an automatic identification approach for landslide dam formation using a real-time abnormal hydrology record and artificial neural network (ANN). In Taiwan, natural dams occur frequently and have the potential to cause significant disasters, especially in the presence of fragile geological conditions such as steep mountains, earthquakes, intense typhoon, and storm events. During a river blockage event, obtaining immediate real-time information can be difficult or even impossible due to extreme weather conditions, remote locations, and the mountainous terrain of the area. As a result of these issues, there is a strong need for a practical tool that can not only detect the occurrence of this type of natural dam but also estimate the potential real-time risk of dam failure. In order to develop the appropriate safety response, a rapid preliminary assessment of the failure type must be identified, followed by a thorough investigation into the failure process after the event has occurred. In this study, a new method is developed that applies an ANN model (1) to evaluate, in real time, the occurrence of a natural dam and the storage changes behind that dam; (2) to rapidly assess the natural dam failure type and failure process; and (3) to reconstruct the dam height variation during the dam failure process after the event. The proposed methodology is applied to Chi-Shan River Basin located in Kaohsiung, Taiwan, during Typhoon Morakot in 2009. In 3 days, Typhoon Morakot produced 1,911 mm of precipitation. The typhoon event produced a landslide dam and a debris flow that inundated Hsiaolin Village, causing 398 deaths. The tragic results of this case study demonstrate the need for an applicable, accurate, and efficient method to evaluate dam formation and its failure rate.
abstract_unstemmed	Abstract This study proposes an automatic identification approach for landslide dam formation using a real-time abnormal hydrology record and artificial neural network (ANN). In Taiwan, natural dams occur frequently and have the potential to cause significant disasters, especially in the presence of fragile geological conditions such as steep mountains, earthquakes, intense typhoon, and storm events. During a river blockage event, obtaining immediate real-time information can be difficult or even impossible due to extreme weather conditions, remote locations, and the mountainous terrain of the area. As a result of these issues, there is a strong need for a practical tool that can not only detect the occurrence of this type of natural dam but also estimate the potential real-time risk of dam failure. In order to develop the appropriate safety response, a rapid preliminary assessment of the failure type must be identified, followed by a thorough investigation into the failure process after the event has occurred. In this study, a new method is developed that applies an ANN model (1) to evaluate, in real time, the occurrence of a natural dam and the storage changes behind that dam; (2) to rapidly assess the natural dam failure type and failure process; and (3) to reconstruct the dam height variation during the dam failure process after the event. The proposed methodology is applied to Chi-Shan River Basin located in Kaohsiung, Taiwan, during Typhoon Morakot in 2009. In 3 days, Typhoon Morakot produced 1,911 mm of precipitation. The typhoon event produced a landslide dam and a debris flow that inundated Hsiaolin Village, causing 398 deaths. The tragic results of this case study demonstrate the need for an applicable, accurate, and efficient method to evaluate dam formation and its failure rate.
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container_issue	6
title_short	Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam
url	https://dx.doi.org/10.1007/s10346-013-0441-1
remote_bool	true
author2	Chen, Y.-C. Shieh, C.-L. Kuo, Y.-S.
author2Str	Chen, Y.-C. Shieh, C.-L. Kuo, Y.-S.
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doi_str	10.1007/s10346-013-0441-1
up_date	2024-07-04T03:00:28.580Z
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Using real-time abnormal hydrology observations to identify a river blockage event resulted from a natural dam

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