{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T17:04:23Z","timestamp":1726851863145},"reference-count":59,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T00:00:00Z","timestamp":1701129600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1016\/j.knosys.2023.111198","type":"journal-article","created":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T09:39:07Z","timestamp":1700645947000},"page":"111198","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Spain on fire: A novel wildfire risk assessment model based on image satellite processing and atmospheric information"],"prefix":"10.1016","volume":"283","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-4962-6314","authenticated-orcid":false,"given":"Helena","family":"Liz-L\u00f3pez","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4127-5505","authenticated-orcid":false,"given":"Javier","family":"Huertas-Tato","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4456-9886","authenticated-orcid":false,"given":"Jorge","family":"P\u00e9rez-Aracil","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6342-106X","authenticated-orcid":false,"given":"Carlos","family":"Casanova-Mateo","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4253-4831","authenticated-orcid":false,"given":"Julia","family":"Sanz-Justo","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5051-3475","authenticated-orcid":false,"given":"David","family":"Camacho","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2023.111198_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2021.113769","article-title":"Elevation in wildfire frequencies with respect to the climate change","volume":"301","author":"Mansoor","year":"2022","journal-title":"J. Environ. Manage."},{"key":"10.1016\/j.knosys.2023.111198_b2","series-title":"IUCN","article-title":"Wildlife in a changing world: an analysis of the 2008 IUCN red list of threatened species","author":"Vi\u00e9","year":"2009"},{"key":"10.1016\/j.knosys.2023.111198_b3","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.apgeog.2014.01.011","article-title":"Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression","volume":"48","author":"Rodrigues","year":"2014","journal-title":"Appl. Geogr."},{"issue":"1208","key":"10.1016\/j.knosys.2023.111198_b4","article-title":"Spatial and temporal expansion of global wildland fire activity in response to climate change","volume":"13","author":"Senande-Rivera","year":"2022","journal-title":"Nature Commun."},{"key":"10.1016\/j.knosys.2023.111198_b5","doi-asserted-by":"crossref","DOI":"10.1029\/2020RG000726","article-title":"Global and regional trends and drivers of fire under climate change","volume":"60","author":"Jones","year":"2022","journal-title":"Rev. Geophys."},{"key":"10.1016\/j.knosys.2023.111198_b6","first-page":"1","article-title":"Characterization of global wildfire burned area spatiotemporal patterns and underlying climatic causes","volume":"12","author":"Shi","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.knosys.2023.111198_b7","unstructured":"J. Fern\u00e1ndez, C. Fern\u00e1ndez, P. F\u00e9m\u00e9nias, H. Peter, The copernicus sentinel-3 mission, in: ILRS Workshop, 2016, pp. 1\u20134."},{"key":"10.1016\/j.knosys.2023.111198_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.firesaf.2022.103622","article-title":"Empirical records of wildfires caused by firearms use in the United States","author":"Short","year":"2022","journal-title":"Fire Saf. J."},{"key":"10.1016\/j.knosys.2023.111198_b9","series-title":"BoD\u2013Books on Demand","article-title":"Approaches to managing disaster: Assessing hazards, emergencies and disaster impacts","author":"Tiefenbacher","year":"2012"},{"key":"10.1016\/j.knosys.2023.111198_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2022.153021","article-title":"Lightning patterns in the pantanal: Untangling natural and anthropogenic-induced wildfires","volume":"820","author":"Menezes","year":"2022","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.knosys.2023.111198_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2021.152002","article-title":"Assessing the socio-economic and land-cover drivers of wildfire activity and its spatiotemporal distribution in south-central Chile","volume":"810","author":"Pozo","year":"2022","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.knosys.2023.111198_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.foreco.2021.119764","article-title":"Previous wildfires and management treatments moderate subsequent fire severity","volume":"504","author":"Cansler","year":"2022","journal-title":"Forest Ecol. Manag."},{"issue":"68","key":"10.1016\/j.knosys.2023.111198_b13","article-title":"Characterization of wildfires and harvesting forest disturbances and recovery using landsat time series: A case study in mediterranean forests in central Italy","volume":"5","author":"Bonannella","year":"2022","journal-title":"Fire"},{"key":"10.1016\/j.knosys.2023.111198_b14","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.knosys.2023.111198_b15","series-title":"2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)","first-page":"764","article-title":"A systematic review of techniques, tools and applications of machine learning","author":"Dhankar","year":"2021"},{"key":"10.1016\/j.knosys.2023.111198_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110118","article-title":"Accurate long-term air temperature prediction with machine learning models and data reduction techniques","volume":"136","author":"Fister","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.knosys.2023.111198_b17","first-page":"1","article-title":"Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review","author":"Salcedo-Sanz","year":"2023","journal-title":"Theor. Appl. Climatol."},{"issue":"8266","key":"10.1016\/j.knosys.2023.111198_b18","article-title":"Machine learning classification\u2013regression schemes for desert locust presence prediction in western Africa","volume":"13","author":"Cornejo-Bueno","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.knosys.2023.111198_b19","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.12982","article-title":"Self-adaptive-deer hunting optimization-based optimal weighted features and hybrid classifier for automated disease detection in plant leaves","volume":"39","author":"Sahu","year":"2022","journal-title":"Expert Syst."},{"key":"10.1016\/j.knosys.2023.111198_b20","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1071\/WF19058","article-title":"Mesoscale spatiotemporal predictive models of daily human-and lightning-caused wildland fire occurrence in British Columbia","volume":"29","author":"Nadeem","year":"2019","journal-title":"Int. J. Wildland Fire"},{"issue":"105","key":"10.1016\/j.knosys.2023.111198_b21","article-title":"A machine learning-based approach for wildfire susceptibility mapping, the case study of the liguria region in Italy","volume":"10","author":"Tonini","year":"2020","journal-title":"Geosciences"},{"key":"10.1016\/j.knosys.2023.111198_b22","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1007\/s10694-019-00846-4","article-title":"Wildland fire spread modeling using convolutional neural networks","volume":"55","author":"Hodges","year":"2019","journal-title":"Fire Technol."},{"key":"10.1016\/j.knosys.2023.111198_b23","series-title":"IJCAI","first-page":"4575","article-title":"Firecast: Leveraging deep learning to predict wildfire spread","author":"Radke","year":"2019"},{"key":"10.1016\/j.knosys.2023.111198_b24","doi-asserted-by":"crossref","first-page":"4007","DOI":"10.1111\/1365-2745.13771","article-title":"Long-term empirical evidence shows post-disturbance climate controls post-fire regeneration","volume":"109","author":"Guz","year":"2021","journal-title":"J. Ecol."},{"issue":"4332","key":"10.1016\/j.knosys.2023.111198_b25","article-title":"Double-step u-net: A deep learning-based approach for the estimation of wildfire damage severity through sentinel-2 satellite data","volume":"10","author":"Farasin","year":"2020","journal-title":"Appl. Sci."},{"key":"10.1016\/j.knosys.2023.111198_b26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-40429-5","article-title":"Using artificial neural networks to predict future dryland responses to human and climate disturbances","volume":"9","author":"Buckland","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.knosys.2023.111198_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2020.142844","article-title":"Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series","volume":"764","author":"Michael","year":"2021","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.knosys.2023.111198_b28","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/11956860.2021.1916213","article-title":"Assessing the probability of wildfire occurrences in a neotropical dry forest","volume":"28","author":"Campos-Vargas","year":"2021","journal-title":"\u00c9coscience"},{"key":"10.1016\/j.knosys.2023.111198_b29","article-title":"Temperature-based fire frequency analysis using machine learning: A case of Changsha, China","volume":"31","author":"Xu","year":"2021","journal-title":"Clim. Risk Manage."},{"issue":"4157","key":"10.1016\/j.knosys.2023.111198_b30","article-title":"A comparison of two machine learning classification methods for remote sensing predictive modeling of the forest fire in the north-eastern Siberia","volume":"12","author":"Janiec","year":"2020","journal-title":"Remote Sens."},{"issue":"5","key":"10.1016\/j.knosys.2023.111198_b31","article-title":"Forest fire probability mapping in eastern serbia: Logistic regression versus random forest method","volume":"12","author":"Milanovi\u0107","year":"2020","journal-title":"Forests"},{"issue":"4199","key":"10.1016\/j.knosys.2023.111198_b32","article-title":"Fire risk assessment models using statistical machine learning and optimized risk indexing","volume":"10","author":"Choi","year":"2020","journal-title":"Appl. Sci."},{"key":"10.1016\/j.knosys.2023.111198_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecolind.2021.107869","article-title":"Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area","volume":"129","author":"Mohajane","year":"2021","journal-title":"Ecol. Indic."},{"key":"10.1016\/j.knosys.2023.111198_b34","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.neucom.2017.04.083","article-title":"Early fire detection using convolutional neural networks during surveillance for effective disaster management","volume":"288","author":"Muhammad","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2023.111198_b35","series-title":"2016 International Forum on Management, Education and Information Technology Application","first-page":"568","article-title":"Deep convolutional neural networks for forest fire detection","author":"Zhang","year":"2016"},{"key":"10.1016\/j.knosys.2023.111198_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2020.112907","article-title":"A machine-learning framework for rapid adaptive digital-twin based fire-propagation simulation in complex environments","volume":"363","author":"Zohdi","year":"2020","journal-title":"Comput. Methods Appl. Mech. Engrg."},{"issue":"294","key":"10.1016\/j.knosys.2023.111198_b37","article-title":"A deep learning approach to downscale geostationary satellite imagery for decision support in high impact wildfires","volume":"12","author":"McCarthy","year":"2021","journal-title":"Forests"},{"key":"10.1016\/j.knosys.2023.111198_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2021.105122","article-title":"Deep fire topology: Understanding the role of landscape spatial patterns in wildfire occurrence using artificial intelligence","volume":"143","author":"Pais","year":"2021","journal-title":"Environ. Model. Softw."},{"key":"10.1016\/j.knosys.2023.111198_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecolind.2021.107735","article-title":"Deep neural networks for global wildfire susceptibility modelling","volume":"127","author":"Zhang","year":"2021","journal-title":"Ecol. Indic."},{"issue":"382","key":"10.1016\/j.knosys.2023.111198_b40","article-title":"Wildland fire susceptibility mapping using support vector regression and adaptive neuro-fuzzy inference system-based whale optimization algorithm and simulated annealing","volume":"10","author":"Al-Fugara","year":"2021","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"10.1016\/j.knosys.2023.111198_b41","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The era5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"10.1016\/j.knosys.2023.111198_b42","doi-asserted-by":"crossref","DOI":"10.1029\/2020GL092194","article-title":"Californian wildfire smoke over Europe: A first example of the aerosol observing capabilities of aeolus compared to ground-based lidar","volume":"48","author":"Baars","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"10.1016\/j.knosys.2023.111198_b43","first-page":"20","article-title":"Addressing biases in near-surface forecasts","volume":"157","author":"Haiden","year":"2018","journal-title":"ECMWF Newslett."},{"key":"10.1016\/j.knosys.2023.111198_b44","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/S0168-1923(03)00038-8","article-title":"Relationship between net radiation and solar radiation for semi-arid shrub-land","volume":"116","author":"Alados","year":"2003","journal-title":"Agricult. Forest Meteorol."},{"key":"10.1016\/j.knosys.2023.111198_b45","series-title":"ECMWF","first-page":"2016","article-title":"Radiation quantities in the ecmwf model and mars","author":"Hogan","year":"2015"},{"key":"10.1016\/j.knosys.2023.111198_b46","first-page":"9501","article-title":"Global trends in downward surface solar radiation from spatial interpolated ground observations during 1961\u20132019","volume":"34","author":"Yuan","year":"2021","journal-title":"J. Clim."},{"key":"10.1016\/j.knosys.2023.111198_b47","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2117325119","article-title":"On the stratospheric chemistry of midlatitude wildfire smoke","volume":"119","author":"Solomon","year":"2022","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.knosys.2023.111198_b48","doi-asserted-by":"crossref","first-page":"2489","DOI":"10.1109\/36.885197","article-title":"Advanced vegetation indices optimized for up-coming sensors: Design, performance, and applications","volume":"38","author":"Gobron","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.knosys.2023.111198_b49","series-title":"Development and Structure of the Canadian Forest Fire Weather Index System, Vol. 35","author":"Van Wagner","year":"1987"},{"key":"10.1016\/j.knosys.2023.111198_b50","doi-asserted-by":"crossref","unstructured":"K. He, X. Chen, S. Xie, Y. Li, R. Doll\u00e1r, Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16000\u201316009.","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"10.1016\/j.knosys.2023.111198_b51","series-title":"Masked autoencoders as spatiotemporal learners","author":"Feichtenhofer","year":"2022"},{"key":"10.1016\/j.knosys.2023.111198_b52","series-title":"Masked autoencoders for point cloud self-supervised learning","author":"Pang","year":"2022"},{"key":"10.1016\/j.knosys.2023.111198_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcp.2021.110841","article-title":"A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder","volume":"451","author":"Kim","year":"2022","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.knosys.2023.111198_b54","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.future.2021.04.007","article-title":"Ensembles of convolutional neural network models for pediatric pneumonia diagnosis","volume":"122","author":"Liz","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.knosys.2023.111198_b55","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1109\/TEVC.2021.3083315","article-title":"Evolutionary neural architecture search for high-dimensional skip-connection structures on densenet style networks","volume":"25","author":"O\u2019Neill","year":"2021","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.knosys.2023.111198_b56","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1139\/er-2020-0019","article-title":"A review of machine learning applications in wildfire science and management","volume":"28","author":"Jain","year":"2020","journal-title":"Environ. Rev."},{"key":"10.1016\/j.knosys.2023.111198_b57","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1007\/s10694-020-01056-z","article-title":"A survey of machine learning algorithms based forest fires prediction and detection systems","volume":"57","author":"Abid","year":"2021","journal-title":"Fire Technol."},{"issue":"15","key":"10.1016\/j.knosys.2023.111198_b58","article-title":"A systematic review of applications of machine learning techniques for wildfire management decision support","volume":"7","author":"Bot","year":"2022","journal-title":"Inventions"},{"key":"10.1016\/j.knosys.2023.111198_b59","first-page":"1","article-title":"A differentiable parallel sampler for efficient video classification","volume":"19","author":"Wang","year":"2023","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705123009486?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705123009486?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T20:34:00Z","timestamp":1701808440000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705123009486"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":59,"alternative-id":["S0950705123009486"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2023.111198","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2024,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Spain on fire: A novel wildfire risk assessment model based on image satellite processing and atmospheric information","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2023.111198","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"111198"}}