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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

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Correction: Costache et al. Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors. <i>Sensors</i> 2021, <i>21</i>, 280.

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Updated: Jul 25, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
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Flood hazard potential evaluation using decision tree state-of-the-art models.

Romulus Costache1,2,3,4, Alireza Arabameri5, Iulia Costache6

  • 1Department of Civil Engineering, Transilvania University of Brasov, Brasov, Romania.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|June 25, 2023
PubMed
Summary

Accurate flood susceptibility mapping using machine learning models in Romania

Keywords:
RomaniaTrotuș River basindecision Tree modelsflood susceptibilitylogistic regression

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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Hydrology

Background:

  • Floods are frequent and devastating natural disasters globally.
  • Accurate flood susceptibility identification is crucial for damage reduction.
  • Quantitative flood susceptibility calculation is a growing research area.

Purpose of the Study:

  • To accurately map flood susceptibility zones in the Trotus River basin.
  • To evaluate the performance of various machine learning algorithms for flood zonation.
  • To identify areas with high flood susceptibility in the study region.

Main Methods:

  • Application of machine learning algorithms: forest-PA-WOE, best first decision tree-WOE, alternating decision tree-WOE, and logistic regression-WOE.
  • Utilizing geographic information system (GIS) for spatial analysis.
  • Quantitative calculation and comparison of model performances.

Main Results:

  • The forest-PA-WOE model achieved the highest accuracy (0.981).
  • Over 16.22% of the Trotus basin is exposed to high and very high flood susceptibility.
  • Model performances surpassed previous studies in the same area.

Conclusions:

  • Machine learning models, particularly forest-PA-WOE, are highly effective for flood susceptibility zonation.
  • The developed models can be reliably applied in data-scarce regions.
  • Findings support watershed managers and hazard authorities in flood risk mitigation and planning.