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

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, wh...

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Main Authors:	Papakosta P, Straub D
Format:	Article
Language:	English
Published:	Italian Society of Silviculture and Forest Ecology (SISEF) 2017-02-01
Series:	iForest - Biogeosciences and Forestry
Subjects:	Fire Occurrence Prediction Canadian Forest Fire Weather Index Poisson Regression
Online Access:	https://iforest.sisef.org/contents/?id=ifor1686-009

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record_format	Article
spelling	doaj-6878a20562d448008342958fbe37604f2020-11-24T21:39:39ZengItalian Society of Silviculture and Forest Ecology (SISEF)iForest - Biogeosciences and Forestry1971-74581971-74582017-02-01101324010.3832/ifor1686-0091686Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal dataPapakosta P0Straub D1Engineering Risk Analysis Group, Technische Universität München , Theresienstr. 90, D-80333 München (Germany)Engineering Risk Analysis Group, Technische Universität München , Theresienstr. 90, D-80333 München (Germany)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.https://iforest.sisef.org/contents/?id=ifor1686-009Fire OccurrencePredictionCanadian Forest Fire Weather IndexPoisson Regression
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Papakosta P Straub D
spellingShingle	Papakosta P Straub D Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data iForest - Biogeosciences and Forestry Fire Occurrence Prediction Canadian Forest Fire Weather Index Poisson Regression
author_facet	Papakosta P Straub D
author_sort	Papakosta P
title	Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data
title_short	Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data
title_full	Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data
title_fullStr	Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data
title_full_unstemmed	Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data
title_sort	probabilistic prediction of daily fire occurrence in the mediterranean with readily available spatio-temporal data
publisher	Italian Society of Silviculture and Forest Ecology (SISEF)
series	iForest - Biogeosciences and Forestry
issn	1971-7458 1971-7458
publishDate	2017-02-01
description	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.
topic	Fire Occurrence Prediction Canadian Forest Fire Weather Index Poisson Regression
url	https://iforest.sisef.org/contents/?id=ifor1686-009
work_keys_str_mv	AT papakostap probabilisticpredictionofdailyfireoccurrenceinthemediterraneanwithreadilyavailablespatiotemporaldata AT straubd probabilisticpredictionofdailyfireoccurrenceinthemediterraneanwithreadilyavailablespatiotemporaldata
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Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data

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