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Applications of GIS: Disaster Management and Emergency Response01:29

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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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Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing

Romulus Costache1,2, Alireza Arabameri3, Thomas Blaschke4

  • 1Research Institute of the University of Bucharest, 90-92 Sos. Panduri, 5th District, 050663 Bucharest, Romania.

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|January 7, 2021
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Summary

Remote sensing and GIS data were used with machine learning models to assess flash-flood potential in Romania. The Deep Learning Neural Network-Weights of Evidence (DLNN-WOE) model showed the highest accuracy in predicting flood susceptibility.

Keywords:
alternating decision treesbivariate statisticsdeep learning neural networkensemble modelsflash-flood potential indexremote sensing sensors

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Area of Science:

  • Environmental Science
  • Geospatial Analysis
  • Natural Hazard Assessment

Background:

  • Remote sensing and GIS are increasingly vital for natural hazard monitoring.
  • Flash floods pose significant risks, necessitating accurate susceptibility assessments.

Purpose of the Study:

  • To assess flash-flood potential in a Romanian catchment using remote sensing and GIS data.
  • To compare the performance of four ensemble machine learning models for flash-flood susceptibility mapping.

Main Methods:

  • Utilized high-resolution satellite imagery to identify 481 flood-affected and 481 non-affected points.
  • Extracted 10 flash-flood predictors for training machine learning models (70% training, 30% validation).
  • Employed four ensemble models: DLNN-FR, DLNN-WOE, ADT-FR, and ADT-WOE to generate Flash-Flood Potential Index (FFPI) maps.

Main Results:

  • DLNN-WOE demonstrated superior performance with the highest accuracy (0.985) and K-index (0.97).
  • Sensitivity analysis showed DLNN-FR (0.985) as most sensitive, while DLNN-WOE (0.991) had the highest specificity.
  • High to very high flash-flood susceptibility areas ranged from 46.57% (DLNN-FR) to 59.38% (ADT-FR) of the study area.
  • The DLNN-WOE model achieved the highest Area Under the Curve (0.96) on the ROC curve, indicating precise results.

Conclusions:

  • Ensemble machine learning models, particularly DLNN-WOE, are effective tools for flash-flood susceptibility assessment.
  • Remote sensing and GIS data integration provides a robust framework for natural hazard evaluation.
  • Accurate FFPI mapping is crucial for effective flood risk management and mitigation strategies.