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

Lianne Pimenta, Lia Duarte (), Ana Cláudia Teodoro, Norma Beltrão, Dênis Gomes and Renata Oliveira
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Lianne Pimenta: Department of Applied Social Sciences, State University of Pará State, Enéas Pinheiro, 2626-Marco, Belém 66095-015, PA, Brazil
Lia Duarte: Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
Ana Cláudia Teodoro: Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
Norma Beltrão: Department of Applied Social Sciences, State University of Pará State, Enéas Pinheiro, 2626-Marco, Belém 66095-015, PA, Brazil
Dênis Gomes: Department of Applied Social Sciences, State University of Pará State, Enéas Pinheiro, 2626-Marco, Belém 66095-015, PA, Brazil
Renata Oliveira: Department of Applied Social Sciences, State University of Pará State, Enéas Pinheiro, 2626-Marco, Belém 66095-015, PA, Brazil

Land, 2025, vol. 14, issue 8, 1-23

Abstract: 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 km 2 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.

Keywords: urban floods; multi-criteria decisions analysis; spatial analysis; environmental management; susceptibility mapping (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:8:p:1543-:d:1711154

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