GIS-Based Flood Susceptibility Mapping Using AHP in the Urban Amazon: A Case Study of Ananindeua, Brazil

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Detalles Bibliográficos
Publicado en:	Land vol. 14, no. 8 (2025), p. 1543-1566
Autor principal:	Pimenta Lianne
Otros Autores:	Duarte, Lia, Teodoro, Ana Cláudia, Beltrão Norma, Gomes Dênis, Oliveira, Renata
Publicado:	MDPI AG
Materias:	Amazon Basin Brazil Extreme weather Susceptibility Soil types Land use Drainage density Geographic information systems Economic growth Emergency preparedness Urban areas Urban sprawl Landslides & mudslides Decision making Criteria Mapping Risk management Rivers Urbanization Flood management Analytic hierarchy process Urban planning Floods Rainfall Flood mapping Disaster management Hydrology Environmental risk Hydrologic data Algebra Land cover Urban development Environmental quality Disaster risk Drainage Gross Domestic Product > GDP Rain Remote sensing
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	Flood susceptibility mapping is essential for urban planning and disaster risk management, especially in rapidly urbanizing areas exposed to extreme rainfall events. This study applies an integrated approach combining Geographic Information Systems (GIS), map algebra, and the Analytic Hierarchy Process (AHP) to assess flood-prone zones in Ananindeua, Pará, Brazil. Five geoenvironmental criteria—rainfall, land use and land cover (LULC), slope, soil type, and drainage density—were selected and weighted using AHP to generate a composite flood susceptibility index. The results identified rainfall and slope as the most influential criteria, with both contributing to over 184 km2 of high-susceptibility area. Spatial patterns showed that flood-prone zones are concentrated in flat urban areas with high drainage density and extensive impermeable surfaces. CHIRPS rainfall data were validated using Pearson’s correlation (r = 0.83) and the Nash–Sutcliffe efficiency (NS = 0.97), confirming the reliability of the precipitation input. The final susceptibility map, categorized into low, medium, and high classes, was validated using flood events derived from Sentinel-1 SAR data (2019–2025), of which 97.2% occurred in medium- or high-susceptibility zones. These findings demonstrate the model’s strong predictive performance and highlight the role of unplanned urban expansion, land cover changes, and inadequate drainage in increasing flood risk. Although specific to Ananindeua, the proposed methodology can be adapted to other urban areas in Brazil, provided local conditions and data availability are considered.
ISSN:	2073-445X
DOI:	10.3390/land14081543
Fuente:	Publicly Available Content Database