Early adaptive evaluation scheme for data-driven calibration in forest fire spread prediction
Fraga, Edigley (Universitat Autònoma de Barcelona)
Cortés Fité, Ana (Universitat Autònoma de Barcelona)
Cencerrado Barraqué, Andrés (Universitat Autònoma de Barcelona)
Hernández Budé, Porfidio (Universitat Autònoma de Barcelona)
Margalef, Tomàs (Universitat Autònoma de Barcelona)

Imprint:	Cham (Suïssa) : Springer, 2020
Description:	14 pàg.
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.
Grants:	Agencia Estatal de Investigación TIN2017-84553- C2-1-R Generalitat de Catalunya 2017/SGR-313
Rights:	Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.
Language:	Anglès
Series:	Lecture notes in computer science ; 12142
Document:	Capítol de llibre ; recerca ; Versió acceptada per publicar
Subject:	Data driven prediction ; Data uncertainty ; Forest fires ; Urgent computing
Published in:	Computational Science - ICCS 2020, 2020, p. 17-30, ISBN 978-3-030-50433-5

DOI: 10.1007/978-3-030-50433-5_2

Postprint 14 p, 2.7 MB

The record appears in these collections:
Books and collections > Book chapters