{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T17:09:34Z","timestamp":1743095374587,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030870096"},{"type":"electronic","value":"9783030870102"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87010-2_25","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T21:45:47Z","timestamp":1631223947000},"page":"350-359","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["On the Possibility of Using Neural Networks for the Thunderstorm Forecasting"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2625-452X","authenticated-orcid":false,"given":"Elena","family":"Stankova","sequence":"first","affiliation":[]},{"given":"Irina O.","family":"Tokareva","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2191-6072","authenticated-orcid":false,"given":"Natalia V.","family":"Dyachenko","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"25_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/978-3-030-58817-5_7","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2020","author":"EN Stankova","year":"2020","unstructured":"Stankova, E.N., Tokareva, I.O., Dyachenko, N.V.: On the effectiveness of using various machine learning methods for forecasting dangerous convective phenomena. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12254, pp. 82\u201393. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58817-5_7"},{"key":"25_CR2","doi-asserted-by":"publisher","first-page":"20200097","DOI":"10.1098\/rsta.2020.0097","volume":"379","author":"MG Schultz","year":"2021","unstructured":"Schultz, M.G., et al.: Can deep learning beat numerical weather prediction? Phil. Trans. R. Soc. A 379, 20200097 (2021). https:\/\/doi.org\/10.1098\/rsta.2020.0097","journal-title":"Phil. Trans. R. Soc. A"},{"key":"25_CR3","doi-asserted-by":"publisher","first-page":"2797","DOI":"10.5194\/gmd-12-2797-2019","volume":"12","author":"S Scher","year":"2019","unstructured":"Scher, S., Messori, G.: Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground. Geosci. Model Dev. 12, 2797\u20132809 (2019). https:\/\/doi.org\/10.5194\/gmd-12-2797-2019","journal-title":"Geosci. Model Dev."},{"issue":"4","key":"25_CR4","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1175\/1520-0434(1998)013<1194:LPFFAN>2.0.CO;2","volume":"13","author":"RJ Kugliowski","year":"1998","unstructured":"Kugliowski, R.J., Barros, A.P.: Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Weather Forecast. 13(4), 1194\u20131204 (1998)","journal-title":"Weather Forecast."},{"issue":"8","key":"25_CR5","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.5194\/hess-13-1413-2009","volume":"13","author":"NQ Hung","year":"2009","unstructured":"Hung, N.Q., Babel, M.S., Weesakul, S., Tripathi, N.K.: An artificial neural network model for forecasting in Bangkok, Thailand. Hydrol. Earth Syst. Sci. 13(8), 1413\u20131425 (2009)","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"25_CR6","unstructured":"Unwetterklimatologie: Starkregen. https:\/\/www.dwd.de\/DE\/leistungen\/unwetterklima\/starkregen\/starkregen.html. Accessed 30 April 2020"},{"issue":"6-7","key":"25_CR7","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1016\/S0895-7177(00)00272-7","volume":"33","author":"KC Luk","year":"2001","unstructured":"Luk, K.C., Ball, J.E., Sharma, A.: An application of artificial neural networks for rainfall forecasting. Math. Comput. Model. 33(6\u20137), 683\u2013693 (2001). https:\/\/doi.org\/10.1016\/S0895-7177(00)00272-7","journal-title":"Math. Comput. Model."},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Tao, Y., Gao, X., Ihler, A., Sorooshian, S.: Deep neural networks for precipitation estimation from remotely sensed information. In: Proceedings IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 1349\u20131355. IEEE (2016)","DOI":"10.1109\/CEC.2016.7743945"},{"key":"25_CR9","doi-asserted-by":"publisher","first-page":"1271","DOI":"10.1175\/JHM-D-16-0176.1","volume":"18","author":"Y Tao","year":"2017","unstructured":"Tao, Y., Gao, X., Ihler, A., Sorooshian, S., Hsu, K.: Precipitation identification with bispectral satellite information using deep learning approaches. J. Hydrometeor. 18, 1271\u20131283 (2017)","journal-title":"J. Hydrometeor."},{"issue":"3","key":"25_CR10","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1175\/1520-0434(1999)014<0338:PFUANN>2.0.CO;2","volume":"14","author":"T Hall","year":"1999","unstructured":"Hall, T., Brooks, H.E., Doswell, C.A., III.: Precipitation forecasting using a neural network. Weather Forecast. 14(3), 338\u2013345 (1999)","journal-title":"Weather Forecast."},{"key":"25_CR11","unstructured":"Culclasure, Andrew, Using Neural Networks to Provide Local Weather Forecasts\u201d (2013). Electronic Theses and Dissertations. 32. https:\/\/digitalcommons.georgiasouthern.edu\/etd\/32"},{"issue":"7","key":"25_CR12","doi-asserted-by":"publisher","first-page":"962","DOI":"10.3844\/jcssp.2011.962.966","volume":"7","author":"T Santhanam","year":"2011","unstructured":"Santhanam, T., Subhajini, A.C.: An efficient weather forecasting system using radial basis function neural network. J. Comput. Sci. 7(7), 962\u2013966 (2011)","journal-title":"J. Comput. Sci."},{"issue":"5","key":"25_CR13","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1175\/1520-0450(1996)035<0617:ANNFTP>2.0.CO;2","volume":"35","author":"C Marzban","year":"1996","unstructured":"Marzban, C., Stumpf, G.J.: A neural network for tornado prediction based on Doppler radar-derived attributes. J. Appl. Meteorol. 35(5), 617\u2013626 (1996)","journal-title":"J. Appl. Meteorol."},{"key":"25_CR14","doi-asserted-by":"publisher","DOI":"10.2151\/jmsj1965.78.6857","author":"J-J Baik","year":"2000","unstructured":"Baik, J.-J., Paek, J.-S.: A Neural Network Model for predicting typhoon intensity. J. Meteor. Soc. Japan. (2000). https:\/\/doi.org\/10.2151\/jmsj1965.78.6857","journal-title":"J. Meteor. Soc. Japan."},{"key":"25_CR15","doi-asserted-by":"publisher","first-page":"6057","DOI":"10.1038\/s41598-019-42339-y","volume":"9","author":"M Ruettgers","year":"2019","unstructured":"Ruettgers, M., Lee, S., Jeon, S., You, D.: Prediction of a typhoon track using a generative adversarial network and satellite images. Sci. Rep. 9, 6057 (2019). https:\/\/doi.org\/10.1038\/s41598-019-42339-y","journal-title":"Sci. Rep."},{"key":"25_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/978-3-319-62404-4_37","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2017","author":"EN Stankova","year":"2017","unstructured":"Stankova, E.N., Grechko, I.A., Kachalkina, Y.N., Khvatkov, E.V.: Hybrid approach combining model-based method with the technology of machine learning for forecasting of dangerous weather phenomena. In: Gervasi, O., et al. (eds.) ICCSA 2017. LNCS, vol. 10408, pp. 495\u2013504. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-62404-4_37"},{"issue":"1\/2","key":"25_CR17","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1504\/IJBIDM.2019.096793","volume":"14","author":"EN Stankova","year":"2019","unstructured":"Stankova, E.N., Balakshiy, A.V., Petrov, D.A., Korkhov, V.V., Shorov, A.V.: OLAP technology and machine learning as the tools for validation of the numerical models of convective clouds. Int. J. Bus. Intell. Data Min. 14(1\/2), 254 (2019). https:\/\/doi.org\/10.1504\/IJBIDM.2019.096793","journal-title":"Int. J. Bus. Intell. Data Min."},{"key":"25_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1007\/978-3-030-24305-0_61","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2019","author":"EN Stankova","year":"2019","unstructured":"Stankova, E.N., Khvatkov, E.V.: Using boosted k-nearest neighbour algorithm for numerical forecasting of dangerous convective phenomena. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11622, pp. 802\u2013811. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-24305-0_61"},{"key":"25_CR19","unstructured":"Raba, N.O., Stankova, E.N.: Research of influence of compensating descending flow on cloud's life cycle by means of 1.5-dimensional model with 2 cylinders. In: Proceedings of MGO, vol. 559, pp. 192\u2013209 (2009). (in Russian)"},{"key":"25_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1007\/978-3-642-12165-4_11","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2010","author":"N Raba","year":"2010","unstructured":"Raba, N., Stankova, E.: On the possibilities of multi-core processor use for real-time forecast of dangerous convective phenomena. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds.) ICCSA 2010. LNCS, vol. 6017, pp. 130\u2013138. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-12165-4_11"},{"key":"25_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/978-3-642-21934-4_51","volume-title":"Computational Science and Its Applications - ICCSA 2011","author":"NO Raba","year":"2011","unstructured":"Raba, N.O., Stankova, E.N.: On the problem of numerical modeling of dangerous convective phenomena: possibilities of real-time forecast with the help of multi-core processors. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011. LNCS, vol. 6786, pp. 633\u2013642. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21934-4_51"},{"key":"25_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1007\/978-3-642-39640-3_18","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2013","author":"NO Raba","year":"2013","unstructured":"Raba, N.O., Stankova, E.N.: On the effectiveness of using the GPU for numerical solution of stochastic collection equation. In: Murgante, B., et al. (eds.) ICCSA 2013. LNCS, vol. 7975, pp. 248\u2013258. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-39640-3_18"},{"key":"25_CR23","first-page":"114","volume":"26","author":"SP Dudarov","year":"2013","unstructured":"Dudarov, S.P., Diev, A.N.: Neural network modeling based on perceptron complexes withsmall training data sets. Math. Meth. Eng. Technol. 26, 114\u2013116 (2013). (in Russian)","journal-title":"Math. Meth. Eng. Technol."},{"issue":"2","key":"25_CR24","doi-asserted-by":"publisher","first-page":"253","DOI":"10.20537\/2076-7633-2015-7-2-253-262","volume":"7","author":"SP Dudarov","year":"2015","unstructured":"Dudarov, S.P., Diev, A.N., Fedosova, N.A., Koltsova, E.M.: Simulation of properties of composite materials reinforced by carbon nanotubes using perceptron complexes. Comput. Res. Model. 7(2), 253\u2013262 (2015). https:\/\/doi.org\/10.20537\/2076-7633-2015-7-2-253-262","journal-title":"Comput. Res. Model."}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87010-2_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,24]],"date-time":"2021-09-24T17:59:50Z","timestamp":1632506390000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87010-2_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030870096","9783030870102"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87010-2_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cagliari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","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":"Customed version of CyberChair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1588","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":"466","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":"18","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":"29% - 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":"2,5","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":"8","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}