Decision support for long-range, community-based planning to mitigate against and recover from potential multiple disasters
This paper discusses a new mathematical model for community-driven disaster planning that is intended to help decision makers exploit the synergies resulting from simultaneously considering actions focusing on mitigation and efforts geared toward long-term recovery. The model is keyed on enabling lo...
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Veröffentlicht in: | Decision Support Systems 2016-07, Vol.87, p.13-25 |
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creator | Chacko, Josey Rees, Loren Paul Zobel, Christopher W. Rakes, Terry R. Russell, Roberta S. Ragsdale, Cliff T. |
description | This paper discusses a new mathematical model for community-driven disaster planning that is intended to help decision makers exploit the synergies resulting from simultaneously considering actions focusing on mitigation and efforts geared toward long-term recovery. The model is keyed on enabling long-term community resilience in the face of potential disasters of varying types, frequencies, and severities, and the approach's highly iterative nature is facilitated by the model's implementation in the context of a decision support system. Three examples from Mombasa, Kenya, East Africa, are discussed and compared in order to demonstrate the advantages of the new mathematical model over the current ad hoc mitigation and long-term recovery planning approaches that are typically used.
•Our DSS math model plans for risks from multiple, perhaps concurrent, hazard sources.•The DSS model examines dependencies arising under a multi-hazard planning model.•Unlike any other models, we include both long-term mitigation and recovery strategies.•We compare our model with two previous approaches to show benefits of our method. |
doi_str_mv | 10.1016/j.dss.2016.04.005 |
format | Article |
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•Our DSS math model plans for risks from multiple, perhaps concurrent, hazard sources.•The DSS model examines dependencies arising under a multi-hazard planning model.•Unlike any other models, we include both long-term mitigation and recovery strategies.•We compare our model with two previous approaches to show benefits of our method.</description><identifier>ISSN: 0167-9236</identifier><identifier>EISSN: 1873-5797</identifier><identifier>DOI: 10.1016/j.dss.2016.04.005</identifier><identifier>CODEN: DSSYDK</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Communities ; Decision support ; Decision support systems ; Disaster management ; Disaster planning ; Disaster relief ; Disasters ; Emergency preparedness ; Long term planning ; Mathematical models ; Mathematical programming ; Multi-hazard ; Recovery plans ; Resilience ; Studies ; Sustainability</subject><ispartof>Decision Support Systems, 2016-07, Vol.87, p.13-25</ispartof><rights>2016 Elsevier B.V.</rights><rights>Copyright Elsevier Sequoia S.A. Jul 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c358t-bc8f88ba71da4604afe9adf0c90ea6f0de71b5b1bac6fd966a9065f0e9e75c123</citedby><cites>FETCH-LOGICAL-c358t-bc8f88ba71da4604afe9adf0c90ea6f0de71b5b1bac6fd966a9065f0e9e75c123</cites><orcidid>0000-0002-9101-1607</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0167923616300586$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids></links><search><creatorcontrib>Chacko, Josey</creatorcontrib><creatorcontrib>Rees, Loren Paul</creatorcontrib><creatorcontrib>Zobel, Christopher W.</creatorcontrib><creatorcontrib>Rakes, Terry R.</creatorcontrib><creatorcontrib>Russell, Roberta S.</creatorcontrib><creatorcontrib>Ragsdale, Cliff T.</creatorcontrib><title>Decision support for long-range, community-based planning to mitigate against and recover from potential multiple disasters</title><title>Decision Support Systems</title><description>This paper discusses a new mathematical model for community-driven disaster planning that is intended to help decision makers exploit the synergies resulting from simultaneously considering actions focusing on mitigation and efforts geared toward long-term recovery. The model is keyed on enabling long-term community resilience in the face of potential disasters of varying types, frequencies, and severities, and the approach's highly iterative nature is facilitated by the model's implementation in the context of a decision support system. Three examples from Mombasa, Kenya, East Africa, are discussed and compared in order to demonstrate the advantages of the new mathematical model over the current ad hoc mitigation and long-term recovery planning approaches that are typically used.
•Our DSS math model plans for risks from multiple, perhaps concurrent, hazard sources.•The DSS model examines dependencies arising under a multi-hazard planning model.•Unlike any other models, we include both long-term mitigation and recovery strategies.•We compare our model with two previous approaches to show benefits of our method.</description><subject>Communities</subject><subject>Decision support</subject><subject>Decision support systems</subject><subject>Disaster management</subject><subject>Disaster planning</subject><subject>Disaster relief</subject><subject>Disasters</subject><subject>Emergency preparedness</subject><subject>Long term planning</subject><subject>Mathematical models</subject><subject>Mathematical programming</subject><subject>Multi-hazard</subject><subject>Recovery plans</subject><subject>Resilience</subject><subject>Studies</subject><subject>Sustainability</subject><issn>0167-9236</issn><issn>1873-5797</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kUGL1jAQhoMo-Ln6A7wFvHiwNWmbpMWT7OqusOBFzyFNJiUfbVIz6cLinzfL58mDpxmY5x3mnZeQt5y1nHH58dw6xLarbcuGljHxjJz4qPpGqEk9J6c6UM3U9fIleYV4Zkz2apQn8vsGbMCQIsVj31Mu1KdM1xSXJpu4wAdq07YdMZTHZjYIju6riTHEhZZEt1DCYgpQs5gQsVATHc1g0wNk6nPa6J4KxBLMSrdjLWFfgbqABgtkfE1eeLMivPlbr8jPr19-XN81999vv11_vm9sL8bSzHb04zgbxZ0ZJBuMh8k4z-zEwEjPHCg-i5nPxkrvJinNxKTwDCZQwvKuvyLvL3v3nH4dgEVvAS2s1QikAzUfOyEG3su-ou_-Qc_pyLFep7maplGJvmOV4hfK5oSYwes9h83kR82ZfopDn3WNQz_FodmgaxxV8-miger0IUDWaANECy7UhxXtUviP-g91M5Yy</recordid><startdate>201607</startdate><enddate>201607</enddate><creator>Chacko, Josey</creator><creator>Rees, Loren Paul</creator><creator>Zobel, Christopher W.</creator><creator>Rakes, Terry R.</creator><creator>Russell, Roberta S.</creator><creator>Ragsdale, Cliff T.</creator><general>Elsevier B.V</general><general>Elsevier Sequoia S.A</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-9101-1607</orcidid></search><sort><creationdate>201607</creationdate><title>Decision support for long-range, community-based planning to mitigate against and recover from potential multiple disasters</title><author>Chacko, Josey ; Rees, Loren Paul ; Zobel, Christopher W. ; Rakes, Terry R. ; Russell, Roberta S. ; Ragsdale, Cliff T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c358t-bc8f88ba71da4604afe9adf0c90ea6f0de71b5b1bac6fd966a9065f0e9e75c123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Communities</topic><topic>Decision support</topic><topic>Decision support systems</topic><topic>Disaster management</topic><topic>Disaster planning</topic><topic>Disaster relief</topic><topic>Disasters</topic><topic>Emergency preparedness</topic><topic>Long term planning</topic><topic>Mathematical models</topic><topic>Mathematical programming</topic><topic>Multi-hazard</topic><topic>Recovery plans</topic><topic>Resilience</topic><topic>Studies</topic><topic>Sustainability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chacko, Josey</creatorcontrib><creatorcontrib>Rees, Loren Paul</creatorcontrib><creatorcontrib>Zobel, Christopher W.</creatorcontrib><creatorcontrib>Rakes, Terry R.</creatorcontrib><creatorcontrib>Russell, Roberta S.</creatorcontrib><creatorcontrib>Ragsdale, Cliff T.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Decision Support Systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chacko, Josey</au><au>Rees, Loren Paul</au><au>Zobel, Christopher W.</au><au>Rakes, Terry R.</au><au>Russell, Roberta S.</au><au>Ragsdale, Cliff T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Decision support for long-range, community-based planning to mitigate against and recover from potential multiple disasters</atitle><jtitle>Decision Support Systems</jtitle><date>2016-07</date><risdate>2016</risdate><volume>87</volume><spage>13</spage><epage>25</epage><pages>13-25</pages><issn>0167-9236</issn><eissn>1873-5797</eissn><coden>DSSYDK</coden><abstract>This paper discusses a new mathematical model for community-driven disaster planning that is intended to help decision makers exploit the synergies resulting from simultaneously considering actions focusing on mitigation and efforts geared toward long-term recovery. 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•Our DSS math model plans for risks from multiple, perhaps concurrent, hazard sources.•The DSS model examines dependencies arising under a multi-hazard planning model.•Unlike any other models, we include both long-term mitigation and recovery strategies.•We compare our model with two previous approaches to show benefits of our method.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.dss.2016.04.005</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-9101-1607</orcidid></addata></record> |
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subjects | Communities Decision support Decision support systems Disaster management Disaster planning Disaster relief Disasters Emergency preparedness Long term planning Mathematical models Mathematical programming Multi-hazard Recovery plans Resilience Studies Sustainability |
title | Decision support for long-range, community-based planning to mitigate against and recover from potential multiple disasters |
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