Development of an empirical model for predicting peak breach flow of landslide dams considering material composition
The existing empirical models do not consider the influence of material composition of landslide deposits on the peak breach flow due to the uncertainty in the material composition and the randomness of its distribution. In this study, based on the statistical analyses and case comparison, the facto...
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| Veröffentlicht in: | Landslides 2022-06, Vol.19 (6), p.1491-1518 |
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| creator | Shan, Yibo Chen, Shengshui Zhong, Qiming Mei, Shengyao Yang, Meng |
| description | The existing empirical models do not consider the influence of material composition of landslide deposits on the peak breach flow due to the uncertainty in the material composition and the randomness of its distribution. In this study, based on the statistical analyses and case comparison, the factors influencing the peak breach flow were comprehensively investigated. The highlight is the material composition-based classification of landslide deposits of 86 landslide cases with detailed grain-size distribution information. In order to consider the geometric morphology of landslide dams and the potential energy of dammed lakes, as well as the material composition of landslide deposits in an empirical model, a multiple regression method was applied on a database, which comprises of 44 documented landslide dam breach cases. A new empirical model for predicting the peak breach flow of landslide dams was developed. Furthermore, for the same 44 documented landslide dam failures, the predicted peak breach flow obtained by using the existing empirical models for embankment and landslide dams and that obtained by using the newly developed model were compared. The comparison of the root mean square error (
E
rms
) and the multiple coefficient of determination (
R
2
) for each empirical model verifies the accuracy and rationality of the new empirical model. Furthermore, for fair validation, several landslide dam breach cases that occurred in recent years in China and have reliable measured data were also used in another comparison. The results show that the new empirical model can reasonably predict the peak breach flow, and exhibits the best performance among all the existing empirical models for embankment and landslide dam breaching. |
| doi_str_mv | 10.1007/s10346-022-01863-1 |
| format | Article |
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E
rms
) and the multiple coefficient of determination (
R
2
) for each empirical model verifies the accuracy and rationality of the new empirical model. Furthermore, for fair validation, several landslide dam breach cases that occurred in recent years in China and have reliable measured data were also used in another comparison. The results show that the new empirical model can reasonably predict the peak breach flow, and exhibits the best performance among all the existing empirical models for embankment and landslide dam breaching.</description><identifier>ISSN: 1612-510X</identifier><identifier>EISSN: 1612-5118</identifier><identifier>DOI: 10.1007/s10346-022-01863-1</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Agriculture ; Civil Engineering ; Composition ; Dam breaching ; Dam failure ; Dams ; Deposits ; Earth and Environmental Science ; Earth Sciences ; Earthquakes ; Embankments ; Empirical analysis ; Empirical models ; Flow ; Flow velocity ; Geography ; Grain size ; Grain size distribution ; Lakes ; Landslides ; Landslides & mudslides ; Model accuracy ; Modelling ; Morphology ; Natural Hazards ; Potential energy ; Regression analysis ; Regression models ; Size distribution ; Soil erosion ; Statistical analysis ; Statistical methods ; Technical Note ; Variables</subject><ispartof>Landslides, 2022-06, Vol.19 (6), p.1491-1518</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2022</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-p157t-35a29dc9789afb4a481d5f8993932594a6eca7a6ca611543eca451b76e41224f3</cites><orcidid>0000-0001-6077-5252</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10346-022-01863-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10346-022-01863-1$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Shan, Yibo</creatorcontrib><creatorcontrib>Chen, Shengshui</creatorcontrib><creatorcontrib>Zhong, Qiming</creatorcontrib><creatorcontrib>Mei, Shengyao</creatorcontrib><creatorcontrib>Yang, Meng</creatorcontrib><title>Development of an empirical model for predicting peak breach flow of landslide dams considering material composition</title><title>Landslides</title><addtitle>Landslides</addtitle><description>The existing empirical models do not consider the influence of material composition of landslide deposits on the peak breach flow due to the uncertainty in the material composition and the randomness of its distribution. In this study, based on the statistical analyses and case comparison, the factors influencing the peak breach flow were comprehensively investigated. The highlight is the material composition-based classification of landslide deposits of 86 landslide cases with detailed grain-size distribution information. In order to consider the geometric morphology of landslide dams and the potential energy of dammed lakes, as well as the material composition of landslide deposits in an empirical model, a multiple regression method was applied on a database, which comprises of 44 documented landslide dam breach cases. A new empirical model for predicting the peak breach flow of landslide dams was developed. Furthermore, for the same 44 documented landslide dam failures, the predicted peak breach flow obtained by using the existing empirical models for embankment and landslide dams and that obtained by using the newly developed model were compared. The comparison of the root mean square error (
E
rms
) and the multiple coefficient of determination (
R
2
) for each empirical model verifies the accuracy and rationality of the new empirical model. Furthermore, for fair validation, several landslide dam breach cases that occurred in recent years in China and have reliable measured data were also used in another comparison. The results show that the new empirical model can reasonably predict the peak breach flow, and exhibits the best performance among all the existing empirical models for embankment and landslide dam breaching.</description><subject>Agriculture</subject><subject>Civil Engineering</subject><subject>Composition</subject><subject>Dam breaching</subject><subject>Dam failure</subject><subject>Dams</subject><subject>Deposits</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Earthquakes</subject><subject>Embankments</subject><subject>Empirical analysis</subject><subject>Empirical models</subject><subject>Flow</subject><subject>Flow velocity</subject><subject>Geography</subject><subject>Grain size</subject><subject>Grain size distribution</subject><subject>Lakes</subject><subject>Landslides</subject><subject>Landslides & mudslides</subject><subject>Model accuracy</subject><subject>Modelling</subject><subject>Morphology</subject><subject>Natural Hazards</subject><subject>Potential energy</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Size distribution</subject><subject>Soil erosion</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Technical Note</subject><subject>Variables</subject><issn>1612-510X</issn><issn>1612-5118</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpFkE9LxDAQxYMouK5-AU8Bz9VMkqbNUda_IHhR8BZm01Sztk1Muvr1bV3R07wHv3kzPEJOgZ0DY9VFBiakKhjnBYNaiQL2yAIU8KIEqPf_NHs5JEc5bxjjmgm9IOOV-3RdiL0bRhpaigN1ffTJW-xoHxrX0TYkGpNrvB398Eqjw3e6Tg7tG2278DVvdTg0ufONow32mdow5MmkGe9xnMQUZkMfQ_ajD8MxOWixy-7kdy7J88310-queHi8vV9dPhQRymosRIlcN1ZXtcZ2LVHW0JRtrbXQgpdaonIWK1QWFUApxeRkCetKOQmcy1YsydkuN6bwsXV5NJuwTcN00nClRM0FVHKixI7Kcf7YpX8KmJnrNbt6zVSv-anXgPgGtwZvHg</recordid><startdate>20220601</startdate><enddate>20220601</enddate><creator>Shan, Yibo</creator><creator>Chen, Shengshui</creator><creator>Zhong, Qiming</creator><creator>Mei, Shengyao</creator><creator>Yang, Meng</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>3V.</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-6077-5252</orcidid></search><sort><creationdate>20220601</creationdate><title>Development of an empirical model for predicting peak breach flow of landslide dams considering material composition</title><author>Shan, Yibo ; Chen, Shengshui ; Zhong, Qiming ; Mei, Shengyao ; Yang, Meng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p157t-35a29dc9789afb4a481d5f8993932594a6eca7a6ca611543eca451b76e41224f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Agriculture</topic><topic>Civil Engineering</topic><topic>Composition</topic><topic>Dam breaching</topic><topic>Dam failure</topic><topic>Dams</topic><topic>Deposits</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Earthquakes</topic><topic>Embankments</topic><topic>Empirical analysis</topic><topic>Empirical models</topic><topic>Flow</topic><topic>Flow velocity</topic><topic>Geography</topic><topic>Grain size</topic><topic>Grain size distribution</topic><topic>Lakes</topic><topic>Landslides</topic><topic>Landslides & mudslides</topic><topic>Model accuracy</topic><topic>Modelling</topic><topic>Morphology</topic><topic>Natural Hazards</topic><topic>Potential energy</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Size distribution</topic><topic>Soil erosion</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Technical Note</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shan, Yibo</creatorcontrib><creatorcontrib>Chen, Shengshui</creatorcontrib><creatorcontrib>Zhong, Qiming</creatorcontrib><creatorcontrib>Mei, Shengyao</creatorcontrib><creatorcontrib>Yang, Meng</creatorcontrib><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Landslides</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shan, Yibo</au><au>Chen, Shengshui</au><au>Zhong, Qiming</au><au>Mei, Shengyao</au><au>Yang, Meng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development of an empirical model for predicting peak breach flow of landslide dams considering material composition</atitle><jtitle>Landslides</jtitle><stitle>Landslides</stitle><date>2022-06-01</date><risdate>2022</risdate><volume>19</volume><issue>6</issue><spage>1491</spage><epage>1518</epage><pages>1491-1518</pages><issn>1612-510X</issn><eissn>1612-5118</eissn><abstract>The existing empirical models do not consider the influence of material composition of landslide deposits on the peak breach flow due to the uncertainty in the material composition and the randomness of its distribution. In this study, based on the statistical analyses and case comparison, the factors influencing the peak breach flow were comprehensively investigated. The highlight is the material composition-based classification of landslide deposits of 86 landslide cases with detailed grain-size distribution information. In order to consider the geometric morphology of landslide dams and the potential energy of dammed lakes, as well as the material composition of landslide deposits in an empirical model, a multiple regression method was applied on a database, which comprises of 44 documented landslide dam breach cases. A new empirical model for predicting the peak breach flow of landslide dams was developed. Furthermore, for the same 44 documented landslide dam failures, the predicted peak breach flow obtained by using the existing empirical models for embankment and landslide dams and that obtained by using the newly developed model were compared. The comparison of the root mean square error (
E
rms
) and the multiple coefficient of determination (
R
2
) for each empirical model verifies the accuracy and rationality of the new empirical model. Furthermore, for fair validation, several landslide dam breach cases that occurred in recent years in China and have reliable measured data were also used in another comparison. The results show that the new empirical model can reasonably predict the peak breach flow, and exhibits the best performance among all the existing empirical models for embankment and landslide dam breaching.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s10346-022-01863-1</doi><tpages>28</tpages><orcidid>https://orcid.org/0000-0001-6077-5252</orcidid></addata></record> |
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| subjects | Agriculture Civil Engineering Composition Dam breaching Dam failure Dams Deposits Earth and Environmental Science Earth Sciences Earthquakes Embankments Empirical analysis Empirical models Flow Flow velocity Geography Grain size Grain size distribution Lakes Landslides Landslides & mudslides Model accuracy Modelling Morphology Natural Hazards Potential energy Regression analysis Regression models Size distribution Soil erosion Statistical analysis Statistical methods Technical Note Variables |
| title | Development of an empirical model for predicting peak breach flow of landslide dams considering material composition |
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