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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Enhancing flood susceptibility modeling using multi-temporal SAR images, CHIRPS data, and hybrid machine learning

Mostafa Riazi1, Khabat Khosravi2, Kaka Shahedi3

  • 1Department of Civil Engineering, Islamic Azad University of Khomeinishahr, Khomeinishahr, Iran.

The Science of the Total Environment
|February 11, 2023
PubMed
Summary

This study used Sentinel-1 radar data and machine learning to create flood susceptibility maps. Hybrid models, particularly Random Committee (RC-RBF), significantly improved flood prediction accuracy, identifying 12% of the area as high-risk.

Keywords:
BA-RBFCHIRPS dataFlood susceptibility mappingGorganrood watershedMachine learningSentinel-1 SAR

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Area of Science:

  • Hydrology
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Flood susceptibility maps are crucial for urban planning and emergency management.
  • Accurate flood prediction aids in developing effective early warning and mitigation strategies.

Purpose of the Study:

  • To develop and evaluate machine learning models for flood susceptibility mapping using Sentinel-1 SAR data.
  • To assess the importance of various environmental factors in flood occurrence.
  • To compare the performance of standalone and hybrid machine learning models.

Main Methods:

  • Utilized Sentinel-1 dB radar images for delineating flooded areas.
  • Selected 12 geospatial parameters including elevation, NDVI, rainfall, and land use.
  • Employed Mutual Information (MI) for parameter importance assessment.
  • Developed Radial Basis Function (RBF) and hybrid models (Bagging, Random Committee, Random Subspace).

Main Results:

  • Hybrid models demonstrated superior performance compared to the standalone RBF model.
  • Random Committee (RC-RBF) achieved the highest accuracy (AUC = 0.997), followed by BA-RBF (0.996), RSS-RBF (0.992), and RBF (0.975).
  • Approximately 12% of the Goorganrood watershed exhibits high to very high flood susceptibility.

Conclusions:

  • Hybrid machine learning techniques significantly enhance flood susceptibility modeling accuracy.
  • The developed models provide valuable insights for flood risk management in the Goorganrood watershed.
  • Sentinel-1 data combined with advanced ML models is effective for detailed flood hazard assessment.