A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model
The evolution of cover collapse is a severe hazard in karst regions. The main objective of the present work was to develop a novel approach that combined both subjective and objective methodologies to evaluate sinkhole susceptibility. Based on the comprehensive analysis of the mechanisms for sinkhol...
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Veröffentlicht in: | Natural hazards (Dordrecht) 2021, Vol.105 (1), p.405-430 |
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description | The evolution of cover collapse is a severe hazard in karst regions. The main objective of the present work was to develop a novel approach that combined both subjective and objective methodologies to evaluate sinkhole susceptibility. Based on the comprehensive analysis of the mechanisms for sinkholes, a typical subjective method was first built using the analytic hierarchy process (AHP) with a hierarchical structure that included nine factors. Considering the apparent disadvantage of AHP, the catastrophe theory was integrated to determine the weight of the criterion factors. To further improve and avoid the bias of the assignment of weights, the entropy method was then integrated into the model to objectively and reasonably determine the order of the index factors and weights of the sub-factors in the index layer during the calculation of the catastrophe model. The verification results showed that the combination of the subjective and objective approaches was indeed suitable to indicate collapse susceptibility. The sensitivity analysis results indicated that the thickness of the overlying layer and karst development were the most sensitive parameters, as indicated by the high rate value using the subjective method. The karst collapse area was then classified into very high-, high-, medium-, and low-susceptibility areas, which accounted for 20.09%, 19.82%, 38.58%, and 21.51% of the total area in the study region. The extraction of groundwater, especially mine draining, was the most important factor, causing more severe hazards, especially in the very high- and high-susceptibility areas. |
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The main objective of the present work was to develop a novel approach that combined both subjective and objective methodologies to evaluate sinkhole susceptibility. Based on the comprehensive analysis of the mechanisms for sinkholes, a typical subjective method was first built using the analytic hierarchy process (AHP) with a hierarchical structure that included nine factors. Considering the apparent disadvantage of AHP, the catastrophe theory was integrated to determine the weight of the criterion factors. To further improve and avoid the bias of the assignment of weights, the entropy method was then integrated into the model to objectively and reasonably determine the order of the index factors and weights of the sub-factors in the index layer during the calculation of the catastrophe model. The verification results showed that the combination of the subjective and objective approaches was indeed suitable to indicate collapse susceptibility. The sensitivity analysis results indicated that the thickness of the overlying layer and karst development were the most sensitive parameters, as indicated by the high rate value using the subjective method. The karst collapse area was then classified into very high-, high-, medium-, and low-susceptibility areas, which accounted for 20.09%, 19.82%, 38.58%, and 21.51% of the total area in the study region. The extraction of groundwater, especially mine draining, was the most important factor, causing more severe hazards, especially in the very high- and high-susceptibility areas.</description><identifier>ISSN: 0921-030X</identifier><identifier>EISSN: 1573-0840</identifier><identifier>DOI: 10.1007/s11069-020-04317-w</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Analytic hierarchy process ; Aquifers ; Catastrophe theory ; Catastrophic collapse ; Civil Engineering ; Collapse ; Drainage ; Earth and Environmental Science ; Earth Sciences ; Entropy ; Environmental Management ; Geology ; Geophysics/Geodesy ; Geotechnical Engineering & Applied Earth Sciences ; Groundwater ; Groundwater mining ; Hydrogeology ; Karst ; Natural Hazards ; Original Paper ; Parameter sensitivity ; Random variables ; Sensitivity analysis ; Sinkholes ; Structural hierarchy ; Subjectivity ; Susceptibility ; Thickness</subject><ispartof>Natural hazards (Dordrecht), 2021, Vol.105 (1), p.405-430</ispartof><rights>Springer Nature B.V. 2020</rights><rights>Springer Nature B.V. 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a342t-453b5548b0aef28e23a8122b06930364edb5dd2fa344e702e0db5a13066146ee3</citedby><cites>FETCH-LOGICAL-a342t-453b5548b0aef28e23a8122b06930364edb5dd2fa344e702e0db5a13066146ee3</cites><orcidid>0000-0001-5666-9860</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/s11069-020-04317-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11069-020-04317-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids></links><search><creatorcontrib>Wei, Aihua</creatorcontrib><creatorcontrib>Li, Duo</creatorcontrib><creatorcontrib>Zhou, Yahong</creatorcontrib><creatorcontrib>Deng, Qinghai</creatorcontrib><creatorcontrib>Yan, Liangdong</creatorcontrib><title>A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model</title><title>Natural hazards (Dordrecht)</title><addtitle>Nat Hazards</addtitle><description>The evolution of cover collapse is a severe hazard in karst regions. The main objective of the present work was to develop a novel approach that combined both subjective and objective methodologies to evaluate sinkhole susceptibility. Based on the comprehensive analysis of the mechanisms for sinkholes, a typical subjective method was first built using the analytic hierarchy process (AHP) with a hierarchical structure that included nine factors. Considering the apparent disadvantage of AHP, the catastrophe theory was integrated to determine the weight of the criterion factors. To further improve and avoid the bias of the assignment of weights, the entropy method was then integrated into the model to objectively and reasonably determine the order of the index factors and weights of the sub-factors in the index layer during the calculation of the catastrophe model. The verification results showed that the combination of the subjective and objective approaches was indeed suitable to indicate collapse susceptibility. The sensitivity analysis results indicated that the thickness of the overlying layer and karst development were the most sensitive parameters, as indicated by the high rate value using the subjective method. The karst collapse area was then classified into very high-, high-, medium-, and low-susceptibility areas, which accounted for 20.09%, 19.82%, 38.58%, and 21.51% of the total area in the study region. The extraction of groundwater, especially mine draining, was the most important factor, causing more severe hazards, especially in the very high- and high-susceptibility areas.</description><subject>Analytic hierarchy process</subject><subject>Aquifers</subject><subject>Catastrophe theory</subject><subject>Catastrophic collapse</subject><subject>Civil Engineering</subject><subject>Collapse</subject><subject>Drainage</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Entropy</subject><subject>Environmental Management</subject><subject>Geology</subject><subject>Geophysics/Geodesy</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Groundwater</subject><subject>Groundwater mining</subject><subject>Hydrogeology</subject><subject>Karst</subject><subject>Natural Hazards</subject><subject>Original Paper</subject><subject>Parameter sensitivity</subject><subject>Random variables</subject><subject>Sensitivity analysis</subject><subject>Sinkholes</subject><subject>Structural hierarchy</subject><subject>Subjectivity</subject><subject>Susceptibility</subject><subject>Thickness</subject><issn>0921-030X</issn><issn>1573-0840</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</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>eNp9kE1r3DAQhkVpoJuPP5CToNc4HX3Y3j0uoWkDgVxS6E2M7XGsrddyNdoE_4n85qjdQm49DRo978vwCHGp4FoB1F9YKag2BWgowBpVFy8fxEqVtSlgbeGjWMFGqwIM_PwkTpl3AEpVerMSr1s5hWcaZRv2jZ8w-TBJnOcYsB1kH6L8hZFT_h5HnJkkH7ilOfnGjz4tEpmJeU9Tkgf205NMA0mccFySb-XgKWJsh0XmwjaDV7LFhJximAe6ymAnczS_FrkPHY3n4qTHkeni3zwTP26_Pt58L-4fvt3dbO8LNFanwpamKUu7bgCp12vSBtdK6yY7MGAqS11Tdp3uM22pBk2QF6gMVJWyFZE5E5-Pvfmu3wfi5HbhEPPZ7LStjQG90TZT-ki1MTBH6t0c_R7j4hS4P97d0bvL3t1f7-4lh8wxxBmenii-V_8n9QZ7JYmW</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Wei, Aihua</creator><creator>Li, Duo</creator><creator>Zhou, Yahong</creator><creator>Deng, Qinghai</creator><creator>Yan, Liangdong</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</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>AEUYN</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><scope>SOI</scope><orcidid>https://orcid.org/0000-0001-5666-9860</orcidid></search><sort><creationdate>2021</creationdate><title>A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model</title><author>Wei, Aihua ; Li, Duo ; Zhou, Yahong ; Deng, Qinghai ; Yan, Liangdong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a342t-453b5548b0aef28e23a8122b06930364edb5dd2fa344e702e0db5a13066146ee3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Analytic hierarchy process</topic><topic>Aquifers</topic><topic>Catastrophe theory</topic><topic>Catastrophic collapse</topic><topic>Civil Engineering</topic><topic>Collapse</topic><topic>Drainage</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Entropy</topic><topic>Environmental Management</topic><topic>Geology</topic><topic>Geophysics/Geodesy</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Groundwater</topic><topic>Groundwater mining</topic><topic>Hydrogeology</topic><topic>Karst</topic><topic>Natural Hazards</topic><topic>Original Paper</topic><topic>Parameter sensitivity</topic><topic>Random variables</topic><topic>Sensitivity analysis</topic><topic>Sinkholes</topic><topic>Structural hierarchy</topic><topic>Subjectivity</topic><topic>Susceptibility</topic><topic>Thickness</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wei, Aihua</creatorcontrib><creatorcontrib>Li, Duo</creatorcontrib><creatorcontrib>Zhou, Yahong</creatorcontrib><creatorcontrib>Deng, Qinghai</creatorcontrib><creatorcontrib>Yan, Liangdong</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</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 One Sustainability</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><collection>Environment Abstracts</collection><jtitle>Natural hazards (Dordrecht)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wei, Aihua</au><au>Li, Duo</au><au>Zhou, Yahong</au><au>Deng, Qinghai</au><au>Yan, Liangdong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model</atitle><jtitle>Natural hazards (Dordrecht)</jtitle><stitle>Nat Hazards</stitle><date>2021</date><risdate>2021</risdate><volume>105</volume><issue>1</issue><spage>405</spage><epage>430</epage><pages>405-430</pages><issn>0921-030X</issn><eissn>1573-0840</eissn><abstract>The evolution of cover collapse is a severe hazard in karst regions. The main objective of the present work was to develop a novel approach that combined both subjective and objective methodologies to evaluate sinkhole susceptibility. Based on the comprehensive analysis of the mechanisms for sinkholes, a typical subjective method was first built using the analytic hierarchy process (AHP) with a hierarchical structure that included nine factors. Considering the apparent disadvantage of AHP, the catastrophe theory was integrated to determine the weight of the criterion factors. To further improve and avoid the bias of the assignment of weights, the entropy method was then integrated into the model to objectively and reasonably determine the order of the index factors and weights of the sub-factors in the index layer during the calculation of the catastrophe model. The verification results showed that the combination of the subjective and objective approaches was indeed suitable to indicate collapse susceptibility. The sensitivity analysis results indicated that the thickness of the overlying layer and karst development were the most sensitive parameters, as indicated by the high rate value using the subjective method. The karst collapse area was then classified into very high-, high-, medium-, and low-susceptibility areas, which accounted for 20.09%, 19.82%, 38.58%, and 21.51% of the total area in the study region. The extraction of groundwater, especially mine draining, was the most important factor, causing more severe hazards, especially in the very high- and high-susceptibility areas.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11069-020-04317-w</doi><tpages>26</tpages><orcidid>https://orcid.org/0000-0001-5666-9860</orcidid></addata></record> |
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subjects | Analytic hierarchy process Aquifers Catastrophe theory Catastrophic collapse Civil Engineering Collapse Drainage Earth and Environmental Science Earth Sciences Entropy Environmental Management Geology Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Groundwater Groundwater mining Hydrogeology Karst Natural Hazards Original Paper Parameter sensitivity Random variables Sensitivity analysis Sinkholes Structural hierarchy Subjectivity Susceptibility Thickness |
title | A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model |
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