Flood vulnerability mapping using the spatial multi-criteria evaluation (SMCE) method in the Minraleng Watershed, Maros Regency, South Sulawesi

Abstract

This study focuses on the assessment of flood-vulnerable areas in the Minraleng watershed, Maros Regency, where the area experiences floods every year. Spatial analysis in the Geographic Information System (GIS) environment has been applied to estimate flood-vulnerable zones using six relevant physical factors, such as rainfall intensity, slope, Elevation, distance from the rivers, land use and soil type. The relative importance of physical factors has been compared in paired matrices to obtain weight values using the Spatial Multi-Criteria Evaluation (SMCE) method. The result showed that the areas located in Camba sub-district had the high vulnerability. The region with a high and very high vulnerability to flood were spread with an area of 436 ha (0,84 %) and 6.168 ha (11.8%).

Spatial Analysis of Flood Vulnerability Base on Biophysics Factor the Krueng Baro Watershed in Flood Mitigation Efforts at Aceh, Indonesia

“…Areas with a high flood vulnerability level are located in low-lying areas near major rivers, which are the primary contributors of water during floods and are directly related to rainfall intensity. Agricultural land use is also a factor contributing to flood vulnerability [24].…”

A Geospatial Expose of Flood-Risk and Vulnerable Areas in Nigeria

“…The dearth of studies that holistically adopt GIS in planning for flood disaster in Nigeria means there is a crucial need to explore more effectively, the use of recent geospatial methods and data for flood hazard mapping, exposure and vulnerability (Komolafe, Adegboyega & Akinluyi, 2015;Efiong & Ushie, 2019). This highlights the need for flood-risk and vulnerability maps as well as information that would be effectively communicated to decision makers, emergency response units and the public (Yahaya & Abdalla, 2010;Fadhil, Ristya, Oktaviani & Kusratmoko, 2019).…”

“…The MCA enables the combination of different geospatial datasets (elevation, slope, rainfall, and distance to water bodies) to derive results. The MCA approach is more suitable for this study because it combines geographical data and a set of preferences to provide the best results for decision making (Malczewski & Rinner 2015;Fadhil, Ristya, Oktaviani & Kusratmoko, 2019). The flow chart (Figure 2) summarizes the step-by-step approach adopted, highlighting data sets, processes and results derived.…”