Geospatial evaluation of flood susceptibility and vulnerability for sustainable urban planning in Sululta town, Oromia region, Ethiopia: A GIS and remote sensing perspective

Flooding, driven by rapid urbanization and changing climate patterns, poses severe risks to urban resilience and sustainable development in Ethiopia. This study introduces a novel geospatial framework that integrates Geographical Information Systems (GIS), Remote Sensing (RS), and Multi-Criteria Decision Analysis (MCDA) to identify and map areas highly susceptible and vulnerable to flooding in Sululta Town . Unlike earlier studies that relied on single-parameter or qualitative assessments, this approach combines key environmental and socio-economic variables, including land use/land cover, slope, soil type, rainfall, elevation, drainage density, lithology, lineament density, and population distribution using the Analytical Hierarchy Process (AHP) and weighted overlay analysis. The resulting spatial layers revealed that approximately 35.2% and 8.5% of the area fall within high and very high flood hazard zones, respectively, while 33% and 6.8% are categorized as high and very high flood risk zones. Model validation using receiver operating characteristic (ROC) and area under the curve (AUC) analysis achieved an accuracy of 81.7%, confirming the model’s robustness. The study provides new knowledge by quantitatively linking biophysical and demographic factors to spatial flood dynamics, offering a replicable, data-driven framework for flood risk management in rapidly urbanizing African contexts. The outcomes directly advance SDG target 11.3 (inclusive and sustainable urbanization) and SDG target 13.1 (resilience to climate-related hazards) and align with the African Union’s Agenda 2063 Goal 7, which promotes environmentally sustainable and climate-resilient communities.