Early adaptive evaluation scheme for data-driven calibration in forest fire spread prediction

Edigley Fraga*, Ana Cortés, Andrés Cencerrado, Porfidio Hernández, Tomàs Margalef

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Forest fires severally affect many ecosystems every year, leading to large environmental damages, casualties and economic losses. Established and emerging technologies are used to help wildfire analysts determine fire behavior and spread aiming at a more accurate prediction results and efficient use of resources in fire fighting. Natural hazards simulations need to deal with data input uncertainty and their impact on prediction results, usually resorting to compute-intensive calibration techniques. In this paper, we propose a new evaluation technique capable of reducing the overall calibration time by 60% when compared to the current data-driven approaches. This is achieved by means of the proposed adaptive evaluation technique based on a periodic monitoring of the fire spread prediction error $$\epsilon $$ estimated by the normalized symmetric difference for each simulation run. Our new strategy avoid wasting too much computing time running unfit individuals thanks to an early adaptive evaluation.

Original languageAmerican English
Pages (from-to)17-30
Number of pages14
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOIs
Publication statusPublished - 2020

Keywords

  • Data driven prediction
  • Data uncertainty
  • Forest fires
  • Urgent computing

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