Spatially-explicit sensitivity and uncertainty analysis in a MCDA-based flood vulnerability model. Issue 9 (2nd September 2019)

• Record Type: Journal Article
• Authors: de Brito, Mariana Madruga; Almoradie, Adrian; Evers, Mariele
• Is Part Of: International journal of geographical information science. Volume 33:Issue 9(2019)
• Page Start: 1788
• Page End: 1806
• Publication Date: 2019-09-02
• Subjects: Sensitivity analysis -- uncertainty analysis -- spatial -- OAT -- ANP -- python -- GDAL; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1599125
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: This study presents a methodology for conducting sensitivity and uncertainty analysis of a GIS-based multi-criteria model used to assess flood vulnerability in a case study in Brazil. The paper explores the robustness of model outcomes against slight changes in criteria weights. One criterion was varied at-a-time, while others were fixed to their baseline values. An algorithm was developed using Python and a geospatial data abstraction library to automate the variation of weights, implement the ANP (analytic network process) tool, reclassify the raster results, compute the class switches, and generate an uncertainty surface. Results helped to identify highly vulnerable areas that are burdened by high uncertainty and to investigate which criteria contribute to this uncertainty. Overall, the criteria 'houses with improper building material' and 'evacuation drills and training' are the most sensitive ones, thus, requiring more accurate measurements. The sensitivity of these criteria is explained by their weights in the base run, their spatial distribution, and the spatial resolution. These findings can support decision makers to characterize, report, and mitigate uncertainty in vulnerability assessment. The case study results demonstrate that the developed approach is simple, flexible, transparent, and may be applied to other complex spatial problems.