{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:03:12Z","timestamp":1742961792891,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031733437"},{"type":"electronic","value":"9783031733444"}],"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\/978-3-031-73344-4_33","type":"book-chapter","created":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T05:01:40Z","timestamp":1728968500000},"page":"393-404","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ML-Based Weather Forecasting Models: A Comparative Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6517-0402","authenticated-orcid":false,"given":"Ihcene","family":"Djouama","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4773-1268","authenticated-orcid":false,"given":"Nabil","family":"Kadache","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4082-8158","authenticated-orcid":false,"given":"Rachid","family":"Seghir","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"33_CR1","unstructured":"Ben-Bouallegue, Z., et al.: The rise of data-driven weather forecasting (2023)"},{"key":"33_CR2","doi-asserted-by":"publisher","unstructured":"Bi, K., Xie, L., Zhang, H., et al.: Accurate medium-range global weather forecasting with 3d neural networks. Nature 619, 533\u2013538 (2023). https:\/\/doi.org\/10.1038\/s41586-023-06185-3","DOI":"10.1038\/s41586-023-06185-3"},{"key":"33_CR3","unstructured":"Bouallegue, Z.B., Team, T.A.: A new ml model in the ecmwf web charts, 13 December 2023. https:\/\/www.ecmwf.int\/en\/about\/media-centre\/aifs-blog\/2023\/new-ml-model-ecmwf-web-charts"},{"key":"33_CR4","doi-asserted-by":"publisher","unstructured":"Buizza, R., et al.: The development and evaluation process followed at ecmwf to upgrade the integrated forecasting system (ifs) (10\/2018 2018). https:\/\/doi.org\/10.21957\/xzopnhty9. https:\/\/www.ecmwf.int\/node\/18658","DOI":"10.21957\/xzopnhty9"},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L., et al.: Fuxi: a cascade machine learning forecasting system for 15-day global weather forecast (2023)","DOI":"10.1038\/s41612-023-00512-1"},{"key":"33_CR6","doi-asserted-by":"publisher","unstructured":"Chen, L., Han, B., Wang, X., Zhao, J., Yang, W., Yang, Z.: Machine learn- ing methods in weather and climate applications: a survey. Appl. Sci. 13(21) (2023). https:\/\/doi.org\/10.3390\/app132112019. https:\/\/www.mdpi.com\/2076-3417\/13\/21\/12019","DOI":"10.3390\/app132112019"},{"key":"33_CR7","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. CoRR abs\/2010.11929 (2020). https:\/\/arxiv.org\/abs\/2010.11929"},{"key":"33_CR8","unstructured":"Guibas, J., Mardani, M., Li, Z., Tao, A., Anandkumar, A., Catanzaro, B.: Adaptive fourier neural operators: efficient token mixers for transformers. CoRR abs\/2111.13587 (2021). https:\/\/arxiv.org\/abs\/2111.13587"},{"key":"33_CR9","unstructured":"Haiden, T., et al.: Evaluation of ECMWF forecasts, including the 2018 upgrade. European Centre for Medium Range Weather Forecasts Reading, UK (2018)"},{"key":"33_CR10","doi-asserted-by":"publisher","unstructured":"Hersbach, H., et al: The era5 global reanalysis. Q. J. Royal Meteo - rological Soc. May 2020. https:\/\/doi.org\/10.1002\/qj.3803","DOI":"10.1002\/qj.3803"},{"key":"33_CR11","doi-asserted-by":"publisher","unstructured":"Lam, R., et al.: Learning skillful medium-range global weather forecasting. Science 382(6677), 1416\u20131421 (2023). https:\/\/doi.org\/10.1126\/science.adi2336","DOI":"10.1126\/science.adi2336"},{"key":"33_CR12","unstructured":"Pathak, J., et al.: Fourcastnet: a global data-driven high-resolution weather model using adaptive fourier neural operators (2022)"},{"key":"33_CR13","doi-asserted-by":"publisher","unstructured":"Schultz, M., et al.: Can deep learning beat numerical weather prediction? Philos. Trans. Royal Soc. Math. Phys. Eng. Sci. 379, February 2021. https:\/\/doi.org\/10.1098\/rsta.2020.0097","DOI":"10.1098\/rsta.2020.0097"}],"container-title":["Lecture Notes in Networks and Systems","Novel and Intelligent Digital Systems: Proceedings of the 4th International Conference (NiDS 2024)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73344-4_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T05:04:51Z","timestamp":1728968691000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73344-4_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031733437","9783031733444"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73344-4_33","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"16 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NiDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Novel & Intelligent Digital Systems Conferences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"25 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nids2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}