{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:33:03Z","timestamp":1778081583632,"version":"3.51.4"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"25","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["Grant No. 2022YFC3003002"],"award-info":[{"award-number":["Grant No. 2022YFC3003002"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-20400-x","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T05:03:30Z","timestamp":1729573410000},"page":"30307-30328","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Fire spread prediction model based on multi-scale convolutional neural network"],"prefix":"10.1007","volume":"84","author":[{"given":"Shuwen","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanying","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxuan","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0057-9804","authenticated-orcid":false,"given":"Xingdong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanping","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shufa","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dandan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"key":"20400_CR1","doi-asserted-by":"publisher","first-page":"2513","DOI":"10.3390\/rs13132513","volume":"13","author":"M Naderpour","year":"2021","unstructured":"Naderpour M, Rizeei HM, Ramezani F (2021) Forest fire risk prediction: a spatial deep neural network-based framework. Remote Sens 13:2513","journal-title":"Remote Sens"},{"key":"20400_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eiar.2019.02.008","author":"JR Molina","year":"2019","unstructured":"Molina JR, Herrera MA, Silva FRy (2019) Wildfire-induced reduction in the carbon storage of Mediterranean ecosystems: An application to brush and forest fires impacts assessment. Environ Impact Assess Rev. https:\/\/doi.org\/10.1016\/j.eiar.2019.02.008","journal-title":"Environ Impact Assess Rev"},{"issue":"9","key":"20400_CR3","first-page":"3227","volume":"31","author":"C Gao","year":"2020","unstructured":"Gao C, Lin H-L, Hu H-Q, Song H (2020) [A review of models of forest fire occurrence prediction in China]. Ying yong sheng tai xue bao = J Appl Ecol 31(9):3227\u201340","journal-title":"Ying yong sheng tai xue bao = J Appl Ecol"},{"key":"20400_CR4","unstructured":"Dong Q, Fuchun X, Junwei H\u00a0(2021) Climate Change and Plants: Biodiversity, Growth and Interactions. Developing Climate-Resilient Crops"},{"key":"20400_CR5","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.envsoft.2013.04.004","volume":"47","author":"MG Cruz","year":"2013","unstructured":"Cruz MG, Alexander ME (2013) Uncertainty associated with model predictions of surface and crown fire rates of spread. Environ Modell Softw 47:16\u201328","journal-title":"Environ Modell Softw"},{"issue":"2","key":"20400_CR6","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3390\/fire3020018","volume":"3","author":"G Marshall","year":"2020","unstructured":"Marshall G, Thompson DK, Anderson K, Simpson B, Linn R, Schroeder D (2020) The impact of fuel treatments on wildfire behavior in north american boreal fuels: a simulation study using FIRETEC. Fire-Switzerland 3(2):18","journal-title":"Fire-Switzerland"},{"key":"20400_CR7","doi-asserted-by":"publisher","unstructured":"Kevin T, Brett S, Derek C (2008) Phoenix: development and application of a bushfire risk management tool. The Australian journal of emergency management. 23:47\u201354. https:\/\/doi.org\/10.1007\/978-3-540-74492-4_21","DOI":"10.1007\/978-3-540-74492-4_21"},{"issue":"02","key":"20400_CR8","doi-asserted-by":"publisher","first-page":"46","DOI":"10.13348\/j.cnki.sjlyyj.2017.0018.y","volume":"30","author":"F Zhao","year":"2017","unstructured":"Zhao F, Shu L, Zhou R, Zhao F, Wang M (2017) Research progress of forest fire behavior spread model. World For Res 30(02):46\u201350. https:\/\/doi.org\/10.13348\/j.cnki.sjlyyj.2017.0018.y","journal-title":"World For Res"},{"issue":"03","key":"20400_CR9","first-page":"45","volume":"37","author":"S Yang","year":"2021","unstructured":"Yang S, Li N (2021) Research progress of forest fire spread model. Gansu Sci Technol 37(03):45\u201347","journal-title":"Gansu Sci Technol"},{"key":"20400_CR10","unstructured":"Rothermel RC (1972) A mathematical model for predicting fire spread in wildland fuels. Res. Pap. INT-115. Usda Forest Service General Technical Report, 115"},{"key":"20400_CR11","doi-asserted-by":"publisher","first-page":"1543","DOI":"10.1139\/x04-905","volume":"34","author":"BJ Stocks","year":"2004","unstructured":"Stocks BJ, Alexander ME, Lanoville RA (2004) Overview of the International Crown Fire Modelling Experiment (ICFME). Can J For Res 34:1543\u20131547","journal-title":"Can J For Res"},{"key":"20400_CR12","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1111\/emr.12453","volume":"22","author":"DMJS Bowman","year":"2021","unstructured":"Bowman DMJS, French BJ, Williamson GJ, Prior LD (2021) Fire, herbivores and the management of temperate Eucalyptus savanna in Tasmania: Introducing the Beaufront fire \u2013 mammalian herbivore field experiment. Ecol Manag Restor 22:140\u2013151","journal-title":"Ecol Manag Restor"},{"key":"20400_CR13","first-page":"39","volume":"03","author":"Z Wang","year":"1992","unstructured":"Wang Z (1992) General forest fire risk rating system. J Natl Disast 03:39\u201344","journal-title":"J Natl Disast"},{"issue":"C","key":"20400_CR14","doi-asserted-by":"publisher","first-page":"104492","DOI":"10.1016\/j.ssci.2019.09.015","volume":"122","author":"J Hegde","year":"2020","unstructured":"Hegde J, Rokseth B (2020) Applications of machine learning methods for engineering risk assessment \u2013 A review. Saf Sci 122(C):104492","journal-title":"Saf Sci"},{"issue":"3","key":"20400_CR15","doi-asserted-by":"publisher","first-page":"2131","DOI":"10.1007\/s10462-017-9589-8","volume":"52","author":"N Chen","year":"2019","unstructured":"Chen N, Liu W, Bai R, Chen A (2019) Application of computational intelligence technologies in emergency management: a literature review. Artif Intell Rev 52(3):2131\u20132168","journal-title":"Artif Intell Rev"},{"key":"20400_CR16","doi-asserted-by":"publisher","unstructured":"Jain P, Coogan SCP, Subramanian SG, Crowley M, Taylor SW, Flannigan MD (2020) A review of machine learning applications in wildfire science and management. Environ Rev 0(ja). https:\/\/doi.org\/10.1139\/er-2020-0019","DOI":"10.1139\/er-2020-0019"},{"key":"20400_CR17","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan MI, Mitchell T (2015) Machine learning: Trends, perspectives, and prospects. Science 349:255\u2013260","journal-title":"Science"},{"key":"20400_CR18","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Commun ACM 60:84\u201390","journal-title":"Commun ACM"},{"key":"20400_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10694-020-01028-3","volume":"57","author":"Z Li","year":"2020","unstructured":"Li Z, Huang Y, Li X, Xu L (2020) Wildland fire burned areas prediction using long short-term memory neural network with attention mechanism. Fire Technol 57:1\u201323","journal-title":"Fire Technol"},{"key":"20400_CR20","doi-asserted-by":"publisher","unstructured":"Radke D, Hessler A, Ellsworth D (2019) FireCast: Leveraging Deep Learning to Predict Wildfire Spread. International Joint Conference on Artificial Intelligence.  https:\/\/doi.org\/10.24963\/ijcai.2019\/636","DOI":"10.24963\/ijcai.2019\/636"},{"key":"20400_CR21","doi-asserted-by":"publisher","unstructured":"John B, Matthew B, Matthias I et al (2020) Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics. https:\/\/doi.org\/10.48550\/arXiv.2012.06679","DOI":"10.48550\/arXiv.2012.06679"},{"key":"20400_CR22","doi-asserted-by":"publisher","unstructured":"Hodges JL, Lattimer BY (2019) Wildland Fire Spread Modeling Using Convolutional Neural Networks. Fire Technol 1\u201328. https:\/\/doi.org\/10.1007\/s10694-019-00846-4","DOI":"10.1007\/s10694-019-00846-4"},{"key":"20400_CR23","doi-asserted-by":"publisher","first-page":"100101","DOI":"10.1016\/j.srs.2023.100101","volume":"8","author":"F Khennou","year":"2023","unstructured":"Khennou F, Akhloufi MA (2023) Improving wildland fire spread prediction using deep U-Nets[J]. Sci Remote Sens 8:100101","journal-title":"Sci Remote Sens"},{"key":"20400_CR24","doi-asserted-by":"publisher","first-page":"483","DOI":"10.5194\/isprs-annals-X-4-W1-2022-483-2023","volume":"10","author":"M Marjani","year":"2023","unstructured":"Marjani M, Mesgari MS (2023) The large-scale wildfire spread prediction using a multi-kernel convolutional neural network[J]. ISPRS Ann Photogram Remote Sens Spatial Inform Sci 10:483\u2013488","journal-title":"ISPRS Ann Photogram Remote Sens Spatial Inform Sci"},{"key":"20400_CR25","doi-asserted-by":"publisher","first-page":"884633","DOI":"10.3389\/feart.2022.884633","volume":"10","author":"P Qi","year":"2022","unstructured":"Qi P, Chang J, Chen X et al (2022) Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM[J]. Front Earth Sci 10:884633","journal-title":"Front Earth Sci"},{"issue":"1","key":"20400_CR26","doi-asserted-by":"publisher","first-page":"86","DOI":"10.3390\/f15010086","volume":"15","author":"X You","year":"2023","unstructured":"You X, Zheng Z, Yang K et al (2023) A PSO-CNN-Based Deep Learning Model for Predicting Forest Fire Risk on a National Scale[J]. Forests 15(1):86","journal-title":"Forests"},{"key":"20400_CR27","doi-asserted-by":"crossref","unstructured":"Kanagaraj K, Amruthaluru U, Selvaganesan J et al (2023) A Dynamic Anomaly Detection Approach for Fault Detection on Fire Alarm System Based on Fuzzy-PSO-CNN Approach[C]\/\/2023 International Conference on Sustainable Communication Networks and Application (ICSCNA). IEEE, pp 1124\u20131129","DOI":"10.1109\/ICSCNA58489.2023.10368601"},{"key":"20400_CR28","first-page":"320","volume":"12306","author":"C Lv","year":"2022","unstructured":"Lv C, Nian J, Yaru X et al (2022) Epilepsy EEG classification and recognition algorithm based on PSO-CNN[C]\/\/Second International Conference on Digital Signal and Computer Communications (DSCC 2022). SPIE 12306:320\u2013324","journal-title":"SPIE"},{"key":"20400_CR29","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1071\/WF14165","volume":"25","author":"JJ O\u2019Brien","year":"2016","unstructured":"O\u2019Brien JJ, Loudermilk EL, Hornsby BS, Hudak AT, Bright BC, Dickinson MB et al (2016) High-resolution infrared thermography for capturing wildland fire behaviour: RxCADRE 2012. Int J Wildland Fire 25:62\u201375","journal-title":"Int J Wildland Fire"},{"key":"20400_CR30","first-page":"146","volume":"22","author":"P Yang","year":"2020","unstructured":"Yang P, Song W, Zhao X, Zheng R, Qingge L (2020) An improved Otsu threshold segmentation algorithm. Int J Comput Sci Eng 22:146\u2013153","journal-title":"Int J Comput Sci Eng"},{"key":"20400_CR31","unstructured":"Maas AL (2013) Rectifier nonlinearities improve neural network acoustic models"},{"key":"20400_CR32","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV), pp 1026-34","DOI":"10.1109\/ICCV.2015.123"},{"key":"20400_CR33","doi-asserted-by":"publisher","first-page":"107146","DOI":"10.1016\/j.patcog.2019.107146","volume":"100","author":"R Soleymani","year":"2020","unstructured":"Soleymani R, Granger E, Fumera G (2020) F-measure curves: A tool to visualize classifier performance under imbalance. Pattern Recognit 100:107146","journal-title":"Pattern Recognit"},{"key":"20400_CR34","doi-asserted-by":"publisher","unstructured":"Ting TO, Yang XS, Cheng S, Huang K (2015) Hybrid metaheuristic algorithms: past, present, and future. Recent advances in swarm intelligence and evolutionary computation. https:\/\/doi.org\/10.1007\/978-3-319-13826-8_4","DOI":"10.1007\/978-3-319-13826-8_4"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20400-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-20400-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-20400-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T23:42:15Z","timestamp":1757115735000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-20400-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,22]]},"references-count":34,"journal-issue":{"issue":"25","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["20400"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-20400-x","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,22]]},"assertion":[{"value":"14 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}