{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T07:50:27Z","timestamp":1768895427807,"version":"3.49.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030198220","type":"print"},{"value":"9783030198237","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-19823-7_32","type":"book-chapter","created":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T00:24:22Z","timestamp":1557879862000},"page":"382-390","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Long-Short Term Memory for an Effective Short-Term Weather Forecasting Model Using Surface Weather Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6909-3546","authenticated-orcid":false,"given":"Pradeep","family":"Hewage","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0276-9000","authenticated-orcid":false,"given":"Ardhendu","family":"Behera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6607-422X","authenticated-orcid":false,"given":"Marcello","family":"Trovati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7273-3295","authenticated-orcid":false,"given":"Ella","family":"Pereira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,12]]},"reference":[{"issue":"4","key":"32_CR1","first-page":"5","volume":"1","author":"M Hayati","year":"2007","unstructured":"Hayati, M., Mohebi, Z.: Application of artificial neural networks for temperature forecasting. Int. J. Electr. Comput. Eng. 1(4), 5 (2007)","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"32_CR2","volume-title":"The Emergence of Numerical Weather Prediction: Richardson\u2019s Dream","author":"P Lynch","year":"2006","unstructured":"Lynch, P.: The Emergence of Numerical Weather Prediction: Richardson\u2019s Dream. Cambridge University Press, Cambridge (2006)"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Oana, L., Spataru, A.: Use of genetic algorithms in numerical weather prediction. In: 2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania, pp. 456\u2013461 (2016)","DOI":"10.1109\/SYNASC.2016.075"},{"key":"32_CR4","unstructured":"WRF Model Users Site (2019). http:\/\/www2.mmm.ucar.edu\/wrf\/users\/. Accessed 21 Jan 2019"},{"issue":"8","key":"32_CR5","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1175\/BAMS-D-15-00308.1","volume":"98","author":"JG Powers","year":"2017","unstructured":"Powers, J.G., et al.: The weather research and forecasting model: overview, system efforts, and future directions. Bull. Am. Meteorol. Soc. 98(8), 1717\u20131737 (2017)","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"32_CR6","unstructured":"Jozefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: ICML, pp. 2342\u20132350 (2015)"},{"issue":"5786","key":"32_CR7","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504\u2013507 (2006)","journal-title":"Science"},{"key":"32_CR8","doi-asserted-by":"crossref","unstructured":"Stensrud, D.J.: Parameterization schemes: keys to understanding numerical weather prediction models (2007)","DOI":"10.1017\/CBO9780511812590"},{"key":"32_CR9","unstructured":"Skamarock, C., et al.: A Description of the Advanced Research WRF Version 3 (2008)"},{"key":"32_CR10","unstructured":"Noaa, Reading GRIB Files (2017). http:\/\/www.cpc.ncep.noaa.gov\/products\/wesley\/reading_grib.html. Accessed 23 Jan 2019"},{"key":"32_CR11","unstructured":"National Centers for Environmental Prediction\/National Weather Service\/NOAA\/U.S. Department of Commerce, NCEP GFS 0.25 Degree Global Forecast Grids Historical Archive, 26 January 2015. https:\/\/rda.ucar.edu\/datasets\/ds084.1\/"},{"key":"32_CR12","doi-asserted-by":"publisher","first-page":"321","DOI":"10.7763\/IJESD.2010.V1.63","volume":"1","author":"SS Baboo","year":"2010","unstructured":"Baboo, S.S., Shereef, I.K.: An efficient weather forecasting system using artificial neural network. Int. J. Environ. Sci. Dev. 1, 321\u2013326 (2010)","journal-title":"Int. J. Environ. Sci. Dev."},{"key":"32_CR13","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.1109\/TGRS.2015.2498971","volume":"54","author":"A Routray","year":"2016","unstructured":"Routray, A., Mohanty, U.C., Osuri, K.K., Kar, S.C., Niyogi, D.: Impact of satellite radiance data on simulations of bay of bengal tropical cyclones using the WRF-3DVAR modeling system. IEEE Trans. Geosci. Remote Sens. 54, 2285\u20132303 (2016)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"32_CR14","volume-title":"Deep Learning with Keras","author":"A Gulli","year":"2017","unstructured":"Gulli, A., Pal, S.: Deep Learning with Keras. Packt Publishing Ltd., Birmingham (2017)"},{"key":"32_CR15","first-page":"298","volume-title":"Lecture Notes in Computer Science","author":"Ardhendu Behera","year":"2019","unstructured":"Behera, A., Keidel, A., Debnath, B.: Context-driven multi-stream LSTM (M-LSTM) for recognizing fine-grained activity of drivers. In: Brox, T., Bruhn, A., Fritz, M. (eds.) Pattern Recognition. GCPR 2018. LNCS, vol. 11269, pp. 298\u2013314. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12939-2_21"},{"key":"32_CR16","unstructured":"Keras, Home - Keras Documentation (2019). https:\/\/keras.io\/. Accessed 28 Jan 2019"},{"key":"32_CR17","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097\u20131105. Curran Associates, Inc. (2012)"},{"key":"32_CR18","unstructured":"Federal Meteorological Handbook Number 1: Surface Weather Observations and Reports, p. 98, November 2017"},{"key":"32_CR19","unstructured":"Metrics - Keras Documentation. https:\/\/keras.io\/metrics\/. Accessed 28 Jan 2019"}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-19823-7_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:33:47Z","timestamp":1709832827000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-19823-7_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030198220","9783030198237"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-19823-7_32","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"12 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hersonissos","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 May 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.aiai2019.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"101","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"49","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"49% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}