Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data

doi:10.3832/ifor1686-009

Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data

iForest - Biogeosciences and Forestry, Volume 10, Issue 1, Pages 32-40 (2016)
doi: https://doi.org/10.3832/ifor1686-009
Published: Oct 06, 2016 - Copyright © 2016 SISEF

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The prediction of wildfire occurrence is an important component of fire management. We have developed probabilistic daily fire prediction models for a Mediterranean region of Europe (Cyprus) at the mesoscale, based on Poisson regression. The models use only readily available spatio-temporal data, which enables their use in an operational setting. Influencing factors included in the models are weather conditions, land cover and human presence. We found that the influence of weather conditions on fire danger in the studied area can be expressed through the FWI component of the Canadian Forest Fire Weather Index System. However, the prediction ability of FWI alone was limited. A model that additionally includes land cover types, population density and road density was found to provide significantly improved predictions. We validated the probabilistic prediction provided by the model with a test data set and illustrate it with maps for selected days.

Keywords

Fire Occurrence, Prediction, Canadian Forest Fire Weather Index, Poisson Regression

Authors’ address

(1)

Engineering Risk Analysis Group, Technische Universität München , Theresienstr. 90, D-80333 München (Germany)

Corresponding author

Panagiota Papakosta
patty.papakosta@gmail.com

Citation

Papakosta P, Straub D (2016). Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data. iForest 10: 32-40. - doi: 10.3832/ifor1686-009

Academic Editor

Davide Ascoli

Paper history

Received: Apr 24, 2015
Accepted: Jul 07, 2016

First online: Oct 06, 2016
Publication Date: Feb 28, 2017
Publication Time: 3.03 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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