Aplicação do método AHP para o mapeamento de risco a inundações a partir de técnicas de geoprocessamento na região metropolitana de Maceió – AL

Abstract

Flooding is a recurrent and high-impact disaster in the Metropolitan Region of Maceió (RMM), driven by unplanned urban expansion, environmental degradation, and deficient infrastructure. The historical recurrence of floods in the region highlights a gap between existing technical knowledge and its effective application in territorial management. This study aimed to map and model flood risk in the RMM through the integration of geoprocessing techniques and the Analytic Hierarchy Process (AHP). For susceptibility analysis, eight criteria were evaluated: elevation, slope, curvature, flow accumulation, distance to drainage, soil type, rainfall, and land use. For vulnerability, seven criteria were considered: household density, population density, distance to highways, presence of children, presence of elderly residents, access to water supply, and sewage services. AHP was used for weighting, with weights objectively determined using the Frequency Ratio (FR) statistical model based on historical flood data. Each criterion was weighted according to its relevance in triggering the phenomenon, followed by normalization and map algebra procedures. The results indicated that 1.3% (37 km2) of the RMM is classified as high or very high risk, with 34 km2 of this area located within urban zones. The municipalities with the highest proportion of urban areas under elevated risk were Maceió, Satuba, and Murici. The analysis revealed that critical infrastructure—such as schools and health facilities—along with dozens of communities and thousands of households, are located within these high-risk zones. In Satuba and in some neighborhoods of Maceió, areas classified as high and very high flood risk were observed even at distances from the drainage network, due to elevated urban vulnerability, as well as in locations protected by flood-control structures. This highlights limitations of the model in representing structural interventions and local particularities. Nevertheless, the method demonstrated strong overall performance, with an AUC of 0.826 and an AHP Consistency Index of 0.05, indicating coherence in the weighting process and effectiveness in identifying risk zones. The study concludes that flood risk in the RMM is highly concentrated in areas of significant social vulnerability, confirming socio-spatial inequality as a determining factor in disaster outcomes.