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Advanced machine learning algorithms for flood susceptibility modeling - performance comparison: Red Sea, Egypt.

Ahmed M Youssef1,2, Hamid Reza Pourghasemi3, Bosy A El-Haddad1

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Machine learning algorithms effectively map flood susceptibility. Random Forest (RF) demonstrated the highest accuracy, outperforming other models in identifying flood-prone areas for environmental management.

Keywords:
Disaster management,Variable importanceEgyptFuture planningMachine learningSustainability

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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Machine Learning Applications

Background:

  • Floods pose significant environmental hazards, necessitating accurate flood susceptibility mapping for sustainable management.
  • Assessing flood-prone areas is crucial for mitigating risks and planning future development.

Purpose of the Study:

  • To evaluate and compare the performance of seven machine learning algorithms (MLAs) for flood susceptibility mapping.
  • To identify the most effective MLA for flood risk assessment in the Safaga-Ras Gharib region, Red Sea, Egypt.

Main Methods:

  • Development of a geospatial database incorporating eleven flood-related factors (e.g., altitude, LULC, TWI).
  • Creation of a flood inventory map using 420 recorded flood locations.
  • Application and comparison of Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Spline (MARS), Boosted Regression Tree (BRT), Functional Data Analysis (FDA), General Linear Model (GLM), and Multivariate Discriminant Analysis (MDA).

Main Results:

  • Random Forest (RF) achieved the highest Area Under Curve (AUC) of 0.813, indicating superior performance.
  • General Linear Model (GLM) and Multivariate Adaptive Regression Spline (MARS) also showed strong performance with AUCs of 0.802 and 0.801, respectively.
  • All tested MLAs provided valuable insights into flood susceptibility, with varying degrees of accuracy.

Conclusions:

  • Machine learning, particularly Random Forest, is a highly effective tool for flood susceptibility mapping.
  • The generated flood susceptibility maps can aid in environmental mitigation, urban planning, and flood control strategies.
  • This study provides a robust framework for applying MLAs to flood risk assessment in data-scarce regions.