A Spatial-Temporal Point Process Model for Estimating Probability of Wildfires in Los Angeles County

Abstract

In Los Angeles County, wildfires are among the most catastrophic environmental events caused by regional characteristics and climate change. In this study, we develop a point process model to estimate the probability of wildfires based on historical weather data and past wildfires data from Los Angeles County from 2004 to 2018. First, we partition Los Angeles County into small rectangular regions, called voxels, with daily temporal resolution. Then, we use random forests and generalized additive models to obtain estimated probabilities on a training data set. In addition to daily weather and fuel-condition measurements, our models incorporate seasonal and geographical effects. Because measurements on weather and fuel conditions are available only from a fixed set of remote automated weather stations, their data must be averaged to relate them to the voxel level, and the way this is done is a factor in modeling. Through the developed model, it is possible to obtain localized, estimated probabilities of wildfires. Ultimately, this tool can aid Los Angeles County Fire Department in improving its capability and effectiveness.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2022
Accession Number
AD1173498

Entities

People

  • Wook Yi

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Air Force
  • Climate Change
  • Combustion
  • Data Analysis
  • Data Sets
  • Fire Protection
  • Fires
  • Forest Fires
  • Geography
  • Information Science
  • Literature Surveys
  • Machine Learning
  • New York
  • Operations Research
  • Probability
  • Test Sets
  • Training
  • Warning Systems
  • Weather Stations
  • Wildfires

Fields of Study

  • Environmental science

Readers

  • Coastal and Marine Engineering/Sediment Transport/Hydraulic Engineering
  • Emergency Management and Homeland Security.
  • Mathematical Modeling and Probability Theory.