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Flood susceptibility mapping using GIS-based composite mapping analysis: a multi-district assessment in Lampung Province, Indonesia

Scientific Nexus · Vol. 1 No. 2 (2025)

DOI
10.35472/scinexus.2410
ISSN
3110-3278
Terbit
2025-12-01
Penerbit
Institut Teknologi Sumatra

Abstrak

Flooding is a recurring hydrometeorological disaster in Lampung Province, Indonesia, particularly affecting Pesawaran Regency, South Lampung Regency, and Bandar Lampung City. This study mapped flood susceptibility using Geographic Information Systems (GIS) with Composite Mapping Analysis (CMA) and scoring methods to support disaster risk reduction planning. Six parameters were analyzed: rainfall, land cover, slope, elevation, soil type, and river buffer distance. Parameter weights were derived objectively through CMA based on spatial analysis of 258 historical flood events (2018-2024). Rainfall data (2015-2024) were interpolated using Inverse Distance Weighting, and spatial analysis was conducted in ArcGIS 10.8. Results show that 90.66% of the study area falls within moderate to high susceptibility classes. Rainfall received the highest weight (20%), followed by elevation (19%), soil type (18%), land cover (16%), river buffer (14%), and slope (13%). Model validation achieved 77.78% accuracy when compared with historical flood locations across 45 sub-districts. High susceptibility areas are characterized by annual rainfall exceeding 2,500 mm, elevations below 50 masl, poor soil infiltration capacity, and dense settlement. The CMA method provides objective parameter weighting while maintaining computational simplicity suitable for resource-constrained settings, offering a practical framework for flood susceptibility assessment in similar tropical regions.

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  • Sumatera Institute of Technology
  • Sriwijaya University
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Rekan penulisOrganisasiArtikel bersama
Ghefira Tsuraya RafliSumatera Institute of Technology1
Sugeng NugrohoAfiliasi tidak diketahui1
Yana Ranita WulandariSumatera Institute of Technology1
Raden PutraSriwijaya University1

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Sumber metadata: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/3110-3278/works, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
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