Optimizing Resource Augmentation for Wildland Fires

Abstract

During December 2017, the Los Angeles County Fire Department (LACoFD) responded to an unprecedented number of wildland fires spanning thousands of square miles within the county. While most of the fires were successfully contained, several caused widespread catastrophic damage. During red flag (high-risk) days, LACoFD currently uses augmented staffing, either by moving on-duty equipment and personnel, or mobilizing those who are off duty to reduce response time. Operational duty chiefs make these augmentation decisions based on current weather conditions and experience. This thesis develops regression models to estimate the probability and potential burned acreage of wildland fires in each of 21 sub-areas in the county. Then, a budget-constrained optimization model reassigns resources between sub-areas in order to minimize expected population displacement due to wildland fire. A comparison of these automated techniques with those manual decisions made during December 2017 reveals significant improvements to augmented staffing that can be made at a lower cost.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2019
Accession Number
AD1080397

Entities

People

  • Zachary T. Scholz

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • California
  • Combustion
  • Fires
  • Forest Fires
  • Geography
  • Humidity
  • Integer Programming
  • Linear Programming
  • Mathematical Models
  • Mathematical Programming
  • Moisture Content
  • Operations Research
  • Optimization
  • Probability
  • Regression Analysis
  • Statistical Analysis
  • United States

Fields of Study

  • Environmental science

Readers

  • Aviation Safety Risk Assessment.
  • Fire Suppression Systems Design.
  • Research Science/Academic Research