Relevance of Error Function in Input Parameter Calibration in a Coupled Wind Field Model-Forest Fire Spread Simulator

Carlos Carrillo, Ana Cortes, Tomas Margalef, Antonio Espinosa, Andres Cencerrado

Research output: Chapter in BookChapterResearchpeer-review

Abstract

Computational fire spread models are a useful tool to mitigate the impact of forest fires. Simulators implementing forest fire spread models require several input parameters to deliver their predictions. Wind speed and direction are sensitive parameters that are usually obtained at low resolution (some kilometers). It is necessary to couple a wind field model, that generates a wind field at high resolution (for example, 30 meters). Even in this case of coupled wind field model-forest fire spread simulator, several parameters include a high degree of uncertainty and usually must be calibrated. One of the strategies consists to introduce a Genetic Algorithm (GA) to calibrate the input parameters according to the actual evolution of the fire. GAs are based on running large amount of simulations, and evaluating and comparing the results provided by each simulation. This fact emphasizes how critical are the metrics used to assess the error of the computational forecasts. The goal of this work is to test eight functions to assess the simulation errors in the case of coupled wind field-forest fire spread prediction to study their drawbacks and advantages to determine which is the most relevant error function.

Original languageAmerican English
Title of host publicationProceedings - 2018 International Conference on High Performance Computing and Simulation, HPCS 2018
EditorsKhalid Zine-Dine, Waleed W. Smari
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages772-779
Number of pages8
ISBN (Electronic)9781538678787
DOIs
Publication statusPublished - 29 Oct 2018

Publication series

NameProceedings - 2018 International Conference on High Performance Computing and Simulation, HPCS 2018

Keywords

  • Data Uncertainty
  • Error Function
  • Prediction
  • Wild Fire

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