GIS-Based Spatial Monte Carlo Analysis for Integrated Flood Management with Two Dimensional Flood Simulation

Honghai Qi (), Pu Qi () and M. Altinakar ()

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2013, vol. 27, issue 10, 3645 pages

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. Copyright Springer Science+Business Media Dordrecht 2013

Keywords: Spatial Monte Carlo Analysis (SMCA); Flood management; Multi-Criteria Decision Making (MCDM); Spatial Compromise Programming (SCP); Remote Sensing; Census block (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11269-013-0370-8

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