{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,7]],"date-time":"2025-04-07T02:49:35Z","timestamp":1743994175968,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819604364"},{"type":"electronic","value":"9789819604371"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-96-0437-1_20","type":"book-chapter","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T16:57:08Z","timestamp":1732640228000},"page":"267-282","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Factor Analysis of Weather Conditions Impact on Firefighter Interventions"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9900-337X","authenticated-orcid":false,"given":"Naoufal","family":"Sirri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0195-4378","authenticated-orcid":false,"given":"Christophe","family":"Guyeux","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"20_CR1","unstructured":"Public Health France. https:\/\/www.santepubliquefrance.fr\/les-actualites\/2022\/climat-et-sante-l-evolution-des-temperatures-a-t-elle-un-impact-sur-la-mortalite-en-france. Accessed on 10 July 2024"},{"key":"20_CR2","first-page":"2786","volume":"53","author":"TT Yang","year":"2017","unstructured":"Yang, T.T., Asanjan, A.A., Welles, E., Gao, X.G., Sorooshian, S., Liu, X.M.: Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information. Adv. Earth Space Sci. 53, 2786\u20132812 (2017)","journal-title":"Adv. Earth Space Sci."},{"key":"20_CR3","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/s13351-019-8162-6","volume":"33","author":"K Zhou","year":"2019","unstructured":"Zhou, K., Zheng, Y., Li, B., Dong, W., Zhang, X.: J. Meteorol. Res. 33, 797\u2013809 (2019)","journal-title":"J. Meteorol. Res."},{"key":"20_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2023.138228","volume":"418","author":"S Singh","year":"2023","unstructured":"Singh, S., Goyal, M.K.: Enhancing climate resilience in businesses: the role of artificial intelligence. J. Clean. Prod. 418, 138228 (2023)","journal-title":"J. Clean. Prod."},{"key":"20_CR5","doi-asserted-by":"publisher","first-page":"661","DOI":"10.3390\/atmos12060661","volume":"12","author":"W Fang","year":"2021","unstructured":"Fang, W., Xue, Q., Shen, L., Sheng, V.S.: Survey on the application of deep learning in extreme weather prediction. Atmosphere 12, 661 (2021)","journal-title":"Atmosphere"},{"issue":"12","key":"20_CR6","doi-asserted-by":"publisher","first-page":"10117","DOI":"10.1007\/s00521-022-06996-x","volume":"34","author":"S Cerna","year":"2022","unstructured":"Cerna, S., Guyeux, C., Laiymani, D.: The usefulness of NLP techniques for predicting peaks in firefighter interventions due to rare events. Neural Comput. Appl. 34(12), 10117\u201310132 (2022)","journal-title":"Neural Comput. Appl."},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Guyeux, C., Makhoul, A., Bahi, J.M.: How to build an optimal and operational knowledge base to predict firefighters\u2019 interventions. In: Proceedings of SAI Intelligent Systems Conference, pp. 558\u2013572. Springer (2022)","DOI":"10.1007\/978-3-031-16072-1_41"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Sirri, Guyeux, Firefighter Intervention Predictive Modeling: Reliability Assessment. In: International Conference on Circuit, Systems and Communication, IEEE , 2024. Accepted for presentation on 28 June 2024","DOI":"10.1109\/ICCSC62074.2024.10617206"},{"key":"20_CR9","unstructured":"Sirri, G.: Air quality impact on firefighter interventions: factors analysis. In: The 7th International Conference on Big Data and Internet of Things. Springer (2024). Accepted for presentation on 17 April 2024"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Sirri, G.: Solar activity impact on firefighter interventions: factors analysis. In: The 5th International Conference on Deep Learning and Applications. Springer (2024). Accepted for presentation on 11 July 2024","DOI":"10.1007\/978-3-031-66694-0_7"},{"key":"20_CR11","unstructured":"Sirri, G.: River levels affecting firefighter interventions: factor analysis. In: The 28th International Database Engineered Applications Symposium. Springer (2024). Proposed for presentation on 28 August 2024"},{"key":"20_CR12","unstructured":"Ministry of Ecological Transition. http:\/\/www.hydro.eaufrance.fr\/. Accessed on 10 July 2024"},{"key":"20_CR13","unstructured":"NASA. https:\/\/www.swpc.noaa.gov\/. Accessed on 10 July 2024"},{"key":"20_CR14","unstructured":"ATMO-BFC. https:\/\/www.atmo-bfc.org\/accueil. Accessed on 10 July 2024"},{"key":"20_CR15","unstructured":"NASA. https:\/\/lance.modaps.eosdis.nasa.gov\/viirs\/. Accessed on 10 July 2024"},{"key":"20_CR16","unstructured":"MODIS. https:\/\/lance.modaps.eosdis.nasa.gov\/modis\/. Accessed on 10 July 2024"},{"key":"20_CR17","unstructured":"Skyfield. https:\/\/github.com\/skyfielders\/python-skyfield. Accessed on 10 July 2024"},{"key":"20_CR18","unstructured":"Astral. https:\/\/pypi.org\/project\/astral\/0.5\/. Accessed on 10 July 2024"},{"key":"20_CR19","unstructured":"The Sentinel Network. https:\/\/www.sentiweb.fr\/?page=table. Accessed on 10 July 2024"},{"key":"20_CR20","unstructured":"Soccer. https:\/\/www.footendirect.com\/. Accessed on 10 July 2024"},{"key":"20_CR21","unstructured":"Ministry of National Education. http:\/\/www.education.gouv.fr\/pid25058\/le-calendrier-scolaire.html. Accessed on 10 July 2024"},{"key":"20_CR22","unstructured":"M\u00e9t\u00e9o-France. https:\/\/www.ecologie.gouv.fr\/. Accessed on 10 July 2024"},{"key":"20_CR23","unstructured":"Meteo-Stat. https:\/\/pypi.org\/project\/meteostat\/. Accessed on 10 July 2024"},{"key":"20_CR24","unstructured":"Vigilance-France. https:\/\/vigilance.meteofrance.fr\/fr. Accessed on 10 July 2024"},{"key":"20_CR25","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"20_CR26","unstructured":"Target Encoder. https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.TargetEncoder.html. Accessed on 10 July 2024"},{"key":"20_CR27","unstructured":"Garreta, R., Moncecchi, G.: Learning scikit-learn: machine learning in Python: experience the benefits of machine learning techniques by applying them to real-world problems using Python and the open source scikit-learn library (2013)"},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Zien, A., Kr\u00e4mer, N., Sonnenburg, S., R\u00e4tsch, G.: The feature importance ranking measure. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2009, Bled, Slovenia, September 7\u201311, 2009, Proceedings, Part II, pp. 694\u2013709. Springer (2009)","DOI":"10.1007\/978-3-642-04174-7_45"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"20_CR30","unstructured":"Ke, G.: Lightgbm: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"}],"container-title":["Communications in Computer and Information Science","Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0437-1_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T17:04:10Z","timestamp":1732640650000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0437-1_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819604364","9789819604371"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0437-1_20","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FDSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Future Data and Security Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Binh Duong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fdse2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/thefdse.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}