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What is Weather?

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Updated: Dec 23, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Big Data and Predictive Analytics in Fire Risk Using Weather Data.

Puneet Agarwal¹, Junlin Tang¹, Adithya Narayanan Lakshmi Narayanan¹

¹Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA.

Risk Analysis : an Official Publication of the Society for Risk Analysis

|April 28, 2020

Summary

Weather significantly impacts fire damage across the US. This study uses big data and machine learning to predict fire risk, finding that the incident location is a key factor in total percent loss.

Keywords:

Big data fire risk gradient boosting tree machine learning weather

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Last Updated: Dec 23, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

Environmental science
Data science
Risk management

Background:

Fire incidents cause significant property and content loss.
Predicting fire risk is crucial for effective management.
Existing models may not fully capture the influence of environmental factors.

Purpose of the Study:

To investigate the impact of weather on fire incident damage in the US.
To develop a predictive model for fire risk using big data analytics.
To identify key factors influencing total percent loss from fires.

Main Methods:

Integration of fire incident data (National Fire Incident Reporting System) and weather data (National Oceanic and Atmospheric Administration).
Utilizing a Gradient Boosting Tree (GBT) machine learning algorithm for predictive modeling.
Calculation of 'Total Percent Loss' as a metric for fire-related damages.

Main Results:

The Gradient Boosting Tree model achieved high predictive accuracy (R² = 0.933, MSE = 124.641).
Model validation showed excellent performance with a strong fit between predicted and actual losses (R² = 0.97).
Analysis revealed that the state of the fire incident is a primary determinant of fire risk.

Conclusions:

Big data and predictive analytics offer a robust framework for fire risk management.
Weather data, when combined with incident data, enhances fire risk prediction.
Understanding the influence of location is vital for mitigating fire-related losses.