000076859 001__ 76859
000076859 005__ 20210409124033.0
000076859 0247_ $$2doi$$a10.1080/19475705.2018.1526219
000076859 0248_ $$2sideral$$a109528
000076859 037__ $$aART-2019-109528
000076859 041__ $$aeng
000076859 100__ $$aMartín, Yago
000076859 245__ $$aModelling temporal variation of fire-occurrence towards the dynamic prediction of human wildfire ignition danger in northeast Spain
000076859 260__ $$c2019
000076859 5060_ $$aAccess copy available to the general public$$fUnrestricted
000076859 5203_ $$aModels of human-caused ignition probability are typically developed from static or structural points of view. This research analyzes the intra-annual dimension of fire occurrence and fire-triggering factors in NE Spain and moves forward towards more accurate predictions. Applying the Maximum Entropy algorithm (MaxEnt) and using wildfire data (2008–2011) and GIS and remote sensing data for the explanatory variables, we construct eight occurrence data scenarios by splitting wildfire records into the four seasons and then separating each season into working and non-working days. We assess model accuracy using a cross-validation k-fold procedure and an operational validation with 2012 data. Results report a substantial contribution of accessibility across models, often coupled with Land Surface Temperature. In addition, we observe great temporal variability, with WAI strongly influencing winter models, whereas distance to roads stands out during working days. Model performances stand consistently above 0.8 AUC in all temporal scenarios, with outstanding predictive effectiveness during summer months. The comparison among static-to-dynamic approaches reveals superior performance of simulations considering temporal scenarios, with AUC values from 0.7 to 0.85. Overall, we believe our approach is reliable enough to derive dynamic predictions of human-caused fire occurrence.
000076859 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/FJCI-2016-31090
000076859 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000076859 590__ $$a3.333$$b2019
000076859 592__ $$a0.814$$b2019
000076859 591__ $$aGEOSCIENCES, MULTIDISCIPLINARY$$b45 / 198 = 0.227$$c2019$$dQ1$$eT1
000076859 593__ $$aEnvironmental Science (miscellaneous)$$c2019$$dQ1
000076859 591__ $$aWATER RESOURCES$$b17 / 94 = 0.181$$c2019$$dQ1$$eT1
000076859 593__ $$aEarth and Planetary Sciences (miscellaneous)$$c2019$$dQ1
000076859 591__ $$aMETEOROLOGY & ATMOSPHERIC SCIENCES$$b32 / 93 = 0.344$$c2019$$dQ2$$eT2
000076859 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000076859 700__ $$0(orcid)0000-0002-9541-5609$$aZúñiga Antón, María$$uUniversidad de Zaragoza
000076859 700__ $$0(orcid)0000-0002-0477-0796$$aRodrigues Mimbrero, Marcos
000076859 7102_ $$13006$$2435$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Geografía Humana
000076859 773__ $$g10, 1 (2019), 385-411$$pGeomatics, natural hazards & risk$$tGeomatics, natural hazards & risk$$x1947-5705
000076859 8564_ $$s1108638$$uhttps://zaguan.unizar.es/record/76859/files/texto_completo.pdf$$yVersión publicada
000076859 8564_ $$s66098$$uhttps://zaguan.unizar.es/record/76859/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000076859 909CO $$ooai:zaguan.unizar.es:76859$$particulos$$pdriver
000076859 951__ $$a2021-04-09-12:36:12
000076859 980__ $$aARTICLE