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Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood

Seyed Vahid Razavi-Termeh1, Abolghasem Sadeghi-Niaraki1, MyoungBae Seo2

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Summary

This study developed advanced flood susceptibility mapping (FSM) in Iraq using machine learning. The Bagging-GA model demonstrated superior accuracy in predicting flood-prone areas, aiding future flood management strategies.

Keywords:
Flash floodGenetic algorithmParallel ensemble-based machine learningRadar imagerySpatial prediction

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Area of Science:

  • Environmental Science
  • Geographic Information Science
  • Machine Learning Applications

Background:

  • Floods are frequent, destructive natural disasters.
  • Accurate flood susceptibility mapping (FSM) is crucial for disaster risk reduction.
  • Previous methods require enhancement for precise flood prediction.

Purpose of the Study:

  • To analyze flood susceptibility mapping (FSM) in Iraq's Sulaymaniyah province.
  • To compare the performance of four machine learning algorithms for FSM.
  • To identify key factors influencing flood susceptibility.

Main Methods:

  • Employed genetic algorithm (GA) to optimize random forest (RF) and bootstrap aggregation (Bagging) algorithms.
  • Utilized meteorological, satellite (Sentinel-1 SAR), and geographic data for model input.
  • Applied multicollinearity, frequency ratio (FR), and Geodetector for data preprocessing.

Main Results:

  • The Bagging-GA model achieved the highest accuracy (AUC = 0.935) in flood susceptibility modeling.
  • All tested models (RF, Bagging, RF-GA, Bagging-GA) showed high predictive accuracy.
  • The study identified significant factors contributing to flood susceptibility.

Conclusions:

  • Optimized machine learning models, particularly Bagging-GA, offer superior flood susceptibility mapping.
  • The findings provide valuable insights for effective flood risk management in the region.
  • This research contributes to the advancement of AI-driven natural disaster prediction.