Problem statement: Despite ongoing recovery efforts, South Africa continues to suffer severe impacts from flooding, with Mpumalanga experiencing significant loss of life, property damage, and economic setbacks. The increasing frequency and severity of flood events, coupled with the persistent vulnerability of affected communities, highlight a critical gap in identifying and mapping flood-prone areas. This underscores the urgent need for comprehensive, spatially-informed flood susceptibility assessments to support proactive planning and risk reduction in Mpumalanga.
Aim of the study: This study aims to analyse and quantify the relative significance of contributing factors to flood susceptibility and to identify and spatially delineate flood-prone areas in Mpumalanga through the application of a GIS-based Analytic Hierarchy Process (AHP) weighted overlay technique.
Data:
Rainfall, source: The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)
Soil Type, source: Soil and Terrain Database for Southern Africa (SOTERSAF)
Slope, source: Derived from DEM data
Drainage density, source: Department of Water and Sanitation
Topographic Wetness Index (TWI), source: Derived from DEM
Methodology
Identification and Weighting of Contributing Factors
1.1. A square Analytic Hierarchy Process (AHP) pairwise comparison matrix was constructed to determine the relative importance of each flood-contributing factor.
1.2. Normalised weights were calculated using the nine-point scale developed by Saaty (1980) to evaluate the comparative significance of each criterion.
Data Collection and Preprocessing
2.1. Relevant geospatial data layers—rainfall, soil texture, drainage density, and elevation were collected from multiple sources.
2.2. The study area was clipped from the full datasets to define the spatial extent of analysis.
2.3. All datasets were projected to a common coordinate system (WGS84) to ensure spatial alignment during overlay operations.
Data Reclassification
3.1. Thematic layers were reclassified into five flood susceptibility classes:
1 (Low susceptibility)
2 (Moderate susceptibility)
3 (High susceptibility)
4 (Very high susceptibility)
5 (Extremely high susceptibility)
3.2. Vector datasets (e.g., drainage and soil type) were converted to raster format using the Feature to Raster tool in ArcGIS.
3.3. For drainage data, a Multiple Ring Buffer tool was used to create distance-based buffer zones from rivers, which were then reclassified to reflect varying levels of flood risk.
Weighted Overlay Analysis
4.1. A raster-based weighted overlay analysis was conducted using the reclassified thematic layers and AHP-derived weights to generate the flood susceptibility map.
Model Validation
5.1. The resulting flood hazard map was validated by comparing model outputs with independent real-world observations to assess both quantitative accuracy and qualitative reliability.
