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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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Watershed Planning within a Quantitative Scenario Analysis Framework
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Forecasting urban fire severity for enhanced emergency response and resource allocation.

Shao-Lun Lee1, Mei-Hua Hsu2, Yi-Fan Wang3

  • 1Department of Information Management, Asia Eastern University of Science and Technology, New Taipei, Taiwan.

Scientific Reports
|November 26, 2025
PubMed
Summary

Fire departments can improve resource allocation using a new predictive model that estimates fire escalation risk. This data-driven approach, integrating Geographic Information Systems (GIS) data, enhances public safety and operational efficiency.

Keywords:
Fire risk assessmentFirefighting resource allocationPredictive analysisXGBoost

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Area of Science:

  • Urban planning and public safety
  • Data science and predictive analytics
  • Firefighting and emergency response

Background:

  • Effective resource allocation is critical for fire departments to manage incidents efficiently.
  • Existing methods often lack the predictive accuracy and spatial integration needed for optimal decision-making.
  • Fire escalation poses significant risks to property, firefighter safety, and public well-being.

Purpose of the Study:

  • To develop and validate a predictive model for fire escalation likelihood.
  • To integrate Geographic Information Systems (GIS) data for spatially informed resource allocation.
  • To enhance urban firefighting efficiency and public safety through data-driven decision-making.

Main Methods:

  • Analysis of 47,382 fire incidents (2010-2020) from a major city.
  • Development and validation of an XGBoost predictive model using 5-fold cross-validation.
  • Integration of key features including building characteristics, time of day, and GIS data.

Main Results:

  • The XGBoost model achieved 82.7% accuracy, with high true positive (84.3%) and true negative (81.1%) rates for major and ordinary fires, respectively.
  • Identified factors increasing fire escalation risk: older buildings, nighttime, and weekends.
  • Simulations projected potential reductions in property damage (23%), firefighter injuries (18%), and response times (15%).

Conclusions:

  • The study presents a novel integration of predictive analytics (XGBoost) and GIS data for urban firefighting.
  • This combined approach offers superior predictive accuracy and spatially explicit resource allocation compared to previous methods.
  • The model-guided resource allocation promises significant improvements in firefighting efficiency and public safety outcomes.