GIS-Based Spatial Monte Carlo Analysis for Integrated Flood Management with Two Dimensional Flood Simulation
Spatial Monte Carlo Analysis (SMCA) is a newly developed Multi-Criteria Decision Making (MCDM) technique based on Spatial Compromise Programming (SCP) and Monte Carlo Simulation (MCS) technique. In contrast to other conventional MCDM techniques, SMCA has the ability to address uneven spatial distrib...
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| Veröffentlicht in: | Water resources management 2013-08, Vol.27 (10), p.3631-3645 |
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| creator | Qi, Honghai Qi, Pu Altinakar, M. S. |
| description | Spatial Monte Carlo Analysis (SMCA) is a newly developed Multi-Criteria Decision Making (MCDM) technique based on Spatial Compromise Programming (SCP) and Monte Carlo Simulation (MCS) technique. In contrast to other conventional MCDM techniques, SMCA has the ability to address uneven spatial distribution of criteria values in the evaluation and ranking of alternatives under various uncertainties. Using this technique, a new flood management tool has been developed within the framework of widely used GIS software ArcGIS. This tool has a user friendly interface which allows construction of user defined criteria, running of SCP computations under uncertain impacting factors and visualization of results. This tool has also the ability to interact with and use of classified Remote Sensing (RS) image layers, and other GIS feature layers like census block boundaries for flood damage calculation and loss of life estimation. The 100-year flood management strategy for Oconee River near the City of Milledgeville, Georgia, USA is chosen as a case study to demonstrate the capabilities of the software. The test result indicates that this new SMCA tool provides a very versatile environment for spatial comparison of various flood mitigation alternatives by taking into account various uncertainties, which will greatly enhance the quality of the decision making process. This tool can also be easily modified and implemented for solving a large variety of problems related to natural resources planning and management. |
| doi_str_mv | 10.1007/s11269-013-0370-8 |
| format | Article |
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This tool has also the ability to interact with and use of classified Remote Sensing (RS) image layers, and other GIS feature layers like census block boundaries for flood damage calculation and loss of life estimation. The 100-year flood management strategy for Oconee River near the City of Milledgeville, Georgia, USA is chosen as a case study to demonstrate the capabilities of the software. The test result indicates that this new SMCA tool provides a very versatile environment for spatial comparison of various flood mitigation alternatives by taking into account various uncertainties, which will greatly enhance the quality of the decision making process. This tool can also be easily modified and implemented for solving a large variety of problems related to natural resources planning and management.</description><identifier>ISSN: 0920-4741</identifier><identifier>EISSN: 1573-1650</identifier><identifier>DOI: 10.1007/s11269-013-0370-8</identifier><identifier>CODEN: WRMAEJ</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Atmospheric Sciences ; Case studies ; Censuses ; Civil Engineering ; Computer programs ; Computer simulation ; Construction ; Decision making ; Earth and Environmental Science ; Earth Sciences ; Earth, ocean, space ; Engineering and environment geology. Geothermics ; Environment ; Exact sciences and technology ; Expected values ; Flood control ; Flood damage ; Flood management ; Floods ; Freshwater ; Geographic information systems ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Hydrology ; Hydrology. Hydrogeology ; Hydrology/Water Resources ; Management decisions ; Mathematical programming ; Monte Carlo methods ; Monte Carlo simulation ; Multiple criteria decision making ; Natural hazards: prediction, damages, etc ; Natural resources ; Preferences ; Remote sensing ; Resource management ; Software ; Spatial analysis ; Spatial distribution ; Studies ; Water resources ; Water resources management</subject><ispartof>Water resources management, 2013-08, Vol.27 (10), p.3631-3645</ispartof><rights>Springer Science+Business Media Dordrecht 2013</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c412t-74a278c70748163d84d6bd042d541db3b705e908c440bbb955decdcdf936c2503</citedby><cites>FETCH-LOGICAL-c412t-74a278c70748163d84d6bd042d541db3b705e908c440bbb955decdcdf936c2503</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11269-013-0370-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11269-013-0370-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27619380$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Qi, Honghai</creatorcontrib><creatorcontrib>Qi, Pu</creatorcontrib><creatorcontrib>Altinakar, M. S.</creatorcontrib><title>GIS-Based Spatial Monte Carlo Analysis for Integrated Flood Management with Two Dimensional Flood Simulation</title><title>Water resources management</title><addtitle>Water Resour Manage</addtitle><description>Spatial Monte Carlo Analysis (SMCA) is a newly developed Multi-Criteria Decision Making (MCDM) technique based on Spatial Compromise Programming (SCP) and Monte Carlo Simulation (MCS) technique. In contrast to other conventional MCDM techniques, SMCA has the ability to address uneven spatial distribution of criteria values in the evaluation and ranking of alternatives under various uncertainties. Using this technique, a new flood management tool has been developed within the framework of widely used GIS software ArcGIS. This tool has a user friendly interface which allows construction of user defined criteria, running of SCP computations under uncertain impacting factors and visualization of results. 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S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>GIS-Based Spatial Monte Carlo Analysis for Integrated Flood Management with Two Dimensional Flood Simulation</atitle><jtitle>Water resources management</jtitle><stitle>Water Resour Manage</stitle><date>2013-08-01</date><risdate>2013</risdate><volume>27</volume><issue>10</issue><spage>3631</spage><epage>3645</epage><pages>3631-3645</pages><issn>0920-4741</issn><eissn>1573-1650</eissn><coden>WRMAEJ</coden><abstract>Spatial Monte Carlo Analysis (SMCA) is a newly developed Multi-Criteria Decision Making (MCDM) technique based on Spatial Compromise Programming (SCP) and Monte Carlo Simulation (MCS) technique. In contrast to other conventional MCDM techniques, SMCA has the ability to address uneven spatial distribution of criteria values in the evaluation and ranking of alternatives under various uncertainties. Using this technique, a new flood management tool has been developed within the framework of widely used GIS software ArcGIS. This tool has a user friendly interface which allows construction of user defined criteria, running of SCP computations under uncertain impacting factors and visualization of results. This tool has also the ability to interact with and use of classified Remote Sensing (RS) image layers, and other GIS feature layers like census block boundaries for flood damage calculation and loss of life estimation. The 100-year flood management strategy for Oconee River near the City of Milledgeville, Georgia, USA is chosen as a case study to demonstrate the capabilities of the software. The test result indicates that this new SMCA tool provides a very versatile environment for spatial comparison of various flood mitigation alternatives by taking into account various uncertainties, which will greatly enhance the quality of the decision making process. This tool can also be easily modified and implemented for solving a large variety of problems related to natural resources planning and management.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11269-013-0370-8</doi><tpages>15</tpages></addata></record> |
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| subjects | Atmospheric Sciences Case studies Censuses Civil Engineering Computer programs Computer simulation Construction Decision making Earth and Environmental Science Earth Sciences Earth, ocean, space Engineering and environment geology. Geothermics Environment Exact sciences and technology Expected values Flood control Flood damage Flood management Floods Freshwater Geographic information systems Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrology Hydrology. Hydrogeology Hydrology/Water Resources Management decisions Mathematical programming Monte Carlo methods Monte Carlo simulation Multiple criteria decision making Natural hazards: prediction, damages, etc Natural resources Preferences Remote sensing Resource management Software Spatial analysis Spatial distribution Studies Water resources Water resources management |
| title | GIS-Based Spatial Monte Carlo Analysis for Integrated Flood Management with Two Dimensional Flood Simulation |
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