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Enhancing precision flood mapping: Pahang's vulnerability unveiled.

Tahmina Afrose Keya1,2, Siventhiran S Balakrishnan3, Maheswaran Solayappan4

  • 1Department of Community Medicine, AIMST University, Bedong, Kedah, Malaysia.

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|November 7, 2024
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Summary
This summary is machine-generated.

This study developed a flood susceptibility map for Pahang, Malaysia, using ensemble machine learning. Rainfall and elevation were identified as key factors, highlighting areas at high risk for improved flood management.

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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Machine Learning

Background:

  • Pahang, Malaysia, faces severe annual floods causing extensive damage.
  • Effective flood management requires accurate susceptibility mapping.

Purpose of the Study:

  • To create a flood susceptibility map for Pahang using an Ensemble Machine Learning (EML) algorithm.
  • To identify and rank factors influencing flood susceptibility in the region.

Main Methods:

  • Utilized geographic information system (GIS) and ArcGIS software.
  • Employed the Random Forest (RF) model and Feature Selection (FS) for analysis.
  • Analyzed nine geospatial factors including rainfall, elevation, and land use/land cover (LULC).

Main Results:

  • Rainfall and elevation were identified as the most significant flood-influencing factors.
  • 'Very high' and 'high' flood susceptibility classes covered 37.1% and 26.3% of the area, respectively.
  • Eastern, Southern, and central Pahang were identified as high-risk flood zones.

Conclusions:

  • Flood susceptibility mapping using EML provides a robust tool for risk reduction.
  • The findings can inform targeted flood management strategies in vulnerable areas.
  • Advanced methods enhance community resilience to flooding events.