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How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse

Tamal Kanti Saha1, Swades Pal1, Swapan Talukdar1

  • 1Department of Geography, University of Gour Banga, Malda, India.

Journal of Environmental Management
|July 27, 2021
PubMed
Summary

This study investigated how image and digital elevation model (DEM) resolution impacts machine learning flood susceptibility models. High-resolution data is recommended for more accurate flood susceptibility mapping and effective flood management.

Keywords:
Machine learningResolution effectSensitivity analysisValidation and index of flood vulnerability

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Area of Science:

  • Environmental Science
  • Geographic Information Science
  • Machine Learning

Background:

  • Digital Elevation Model (DEM) and spatial resolution effects on flood simulation are known.
  • The impact of coarse vs. fine resolution data on machine learning ensemble flood susceptibility prediction, especially in data-scarce regions, remains under-explored.

Purpose of the Study:

  • To assess the performance of coarse (Landsat, SRTM) and high (Sentinel-2, ALOS PALSAR) resolution data in flood susceptibility models.
  • To develop highly accurate and robust flood susceptibility models using standalone and ensemble machine learning algorithms.
  • To investigate the influence of flood conditioning parameters on flood susceptibility modeling.

Main Methods:

  • Generated fifteen flood conditioning parameters from both coarse and high-resolution datasets.
  • Employed Artificial Neural Network-multilayer perceptron (ANN-MLP), Random Forest (RF), and ensemble algorithms (Bagging-MLP, Bagging-Gaussian Processes, Bagging-SMOreg).
  • Utilized ROC-based sensitivity analysis and an Index of Flood Vulnerability (IFV) model for validation.

Main Results:

  • Coarse resolution MLP model showed high performance (AUC: 0.94), predicting 11.65% very high flood susceptibility zones (FSz).
  • High-resolution MLP model predicted 19.34% very high FSz, outperforming other models and resolutions.
  • Elevation was the dominant factor influencing flood susceptibility, followed by drainage density and flow accumulation.

Conclusions:

  • High-resolution data is recommended for developing accurate machine learning-based flood susceptibility models.
  • The MLP model, particularly with high-resolution data, demonstrated superior performance in predicting flood susceptibility.
  • The findings provide a valuable database for flood management by identifying high-risk areas and key influencing factors.