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|Title:||Using multi-criteria evaluation and GIS for flood risk analysis in informal settlements of Cape Town: the case of graveyard pond||Authors:||Musungu, Kevin
|Keywords:||Informal settlements;Flood risk management.;Multi-criteria evaluation;GIS;Participation;Risk weights||Issue Date:||2012||Publisher:||South African Journal of Geomatics||Source:||Musungu, K., Motala, S. & Smit, J. (2012). Using multi-criteria evaluation and GIS for flood risk analysis in informal settlements of Cape Town: the case of graveyard pond. South African Journal of Geomatics, 1(1): 77-91||Abstract:||Rural-urban migrations have contributed to the steady increase in the population of Cape Town. Many of the migrants have settled in informal settlements because they cannot afford to rent or buy decent housing. Many of these settlements are however located on marginal and often poorly drained land. Consequently, most of these settlements are prone to flooding after prolonged rainfall. Current flood risk management techniques implemented by the authorities of the Cape Town City Council (CTCC) are not designed to support informal settlements. In fact, owing to a lack of information about the levels of flood risk within the individual settlements, either the CTCC has often been uninvolved or it has implemented inappropriate remedies within such settlements. This study sought to investigate a methodology that the CTCC could use to improve flood risk assessment. Using a case study of an informal settlement in Cape Town, this study proposed a methodology of integration of community-based information into a Geographic Information System (GIS) that can be used by the CTCC for risk assessment. In addition, this research demonstrated the use of a participatory multi-criteria evaluation (MCE) for risk assessment. A questionnaire was used to collect community-based information. The shack outlines of the informal settlement were digitized using CTCC aerial imagery. The questionnaires were captured using spreadsheets and linked to the corresponding shacks in the GIS. Risk weights were subsequently calculated using pairwise comparisons for each household, based on their responses to the questionnaires. The risk weights were then mapped in the GIS to show the spatial disparities in risk.||URI:||http://hdl.handle.net/11189/3361||ISSN:||2225-8531|
|Appears in Collections:||Eng - Journal Articles, Faculty of Engineering|
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checked on Nov 24, 2020
checked on Nov 24, 2020
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