{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T04:45:03Z","timestamp":1782794703411,"version":"3.54.5"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032304872","type":"print"},{"value":"9783032304889","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-30488-9_33","type":"book-chapter","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T04:10:22Z","timestamp":1782792622000},"page":"532-549","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A LightGBM Framework for\u00a0Operational Tide Forecasting and\u00a0Predictive Imputation in\u00a0the\u00a0R\u00edo De La Plata"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9735-5193","authenticated-orcid":false,"given":"Diego","family":"Silva Piedra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4285-674X","authenticated-orcid":false,"given":"M\u00f3nica","family":"Fossati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2368-8907","authenticated-orcid":false,"given":"Pablo","family":"Ezzatti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,1]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2623\u20132631. (2019)","DOI":"10.1145\/3292500.3330701"},{"issue":"2","key":"33_CR2","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1016\/j.ijforecast.2020.07.007","volume":"37","author":"CS Bojer","year":"2021","unstructured":"Bojer, C.S., Meldgaard, J.P.: Kaggle forecasting competitions: an overlooked learning opportunity. Int. J. Forecast. 37(2), 587\u2013603 (2021)","journal-title":"Int. J. Forecast."},{"key":"33_CR3","unstructured":"Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: BRITS: bidirectional recurrent imputation for time series. In: Advances in Neural Information Processing Systems. vol.\u00a031 (2018)"},{"key":"33_CR4","unstructured":"Du, W., et al.: TSI-Bench: benchmarking time series imputation (2024). https:\/\/arxiv.org\/abs\/2406.12747"},{"key":"33_CR5","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.5194\/hess-14-1931-2010","volume":"14","author":"A Elshorbagy","year":"2010","unstructured":"Elshorbagy, A., Corzo, G., Srinivasulu, S., Solomatine, D.P.: Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 1: Concepts and methodology. Hydrol. Earth Syst. Sci. 14, 1931\u20131941 (2010)","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"33_CR6","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.5194\/hess-14-1943-2010","volume":"14","author":"A Elshorbagy","year":"2010","unstructured":"Elshorbagy, A., Corzo, G., Srinivasulu, S., Solomatine, D.P.: Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application. Hydrol. Earth Syst. Sci. 14, 1943\u20131961 (2010)","journal-title":"Hydrol. Earth Syst. Sci."},{"issue":"2","key":"33_CR7","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1080\/23863781.2023.2210262","volume":"9","author":"M Fossati","year":"2022","unstructured":"Fossati, M., Balparda, D., Sellanes, L., Silva, D., Jackson, M., Ezzatti, P.: Desarrollo del sistema de pron\u00f3stico del R\u00edo de la Plata y su frente mar\u00edtimo: PronUy_RPFM. Ribagua 9(2), 25\u201340 (2022)","journal-title":"Ribagua"},{"key":"33_CR8","first-page":"48","volume":"1","author":"M Fossati","year":"2014","unstructured":"Fossati, M., et al.: Din\u00e1mica de flujo, del campo salino y de los sedimentos finos en el R\u00edo de la Plata. RIBAGUA - Revista Iberoamericana del Agua 1, 48\u201363 (2014)","journal-title":"RIBAGUA - Revista Iberoamericana del Agua"},{"issue":"5","key":"33_CR9","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189\u20131232 (2001)","journal-title":"Ann. Stat."},{"key":"33_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ocemod.2024.102376","volume":"190","author":"M Gan","year":"2024","unstructured":"Gan, M., et al.: An improved machine learning-based model to predict estuarine water levels. Ocean Model. 190, 102376 (2024)","journal-title":"Ocean Model."},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Grinsztajn, L., Oyallon, E., Varoquaux, G.: Why tree-based models still outperform deep learning on tabular data. In: Advances in Neural Information Processing Systems. vol. 35 (2022)","DOI":"10.52202\/068431-0037"},{"issue":"7","key":"33_CR12","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1016\/S0278-4343(96)00061-1","volume":"17","author":"RA Guerrero","year":"1997","unstructured":"Guerrero, R.A., Acha, E.M., Framin\u00e1n, M.B., Lasta, C.A.: Physical oceanography of the R\u00edo de la Plata Estuary. Argentina. Continental Shelf Res. 17(7), 727\u2013742 (1997). https:\/\/doi.org\/10.1016\/S0278-4343(96)00061-1","journal-title":"Argentina. Continental Shelf Res."},{"key":"33_CR13","unstructured":"Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems. vol. 30, pp. 3146\u20133154 (2017)"},{"issue":"5","key":"33_CR14","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1127\/metz\/2018\/0908","volume":"27","author":"P K\u00f6rner","year":"2018","unstructured":"K\u00f6rner, P., Kronenberg, R., Genzel, S., Bernhofer, C.: Introducing gradient boosting as a universal gap filling tool for meteorological time series. Meteorol. Z. 27(5), 369\u2013376 (2018). https:\/\/doi.org\/10.1127\/metz\/2018\/0908","journal-title":"Meteorol. Z."},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"McElfresh, D., et al.: When do neural nets outperform boosted trees on tabular data? In: Advances in Neural Information Processing Systems. vol.\u00a036 (2023)","DOI":"10.52202\/075280-3337"},{"key":"33_CR16","doi-asserted-by":"publisher","unstructured":"Torres, M., Klapp, J., Gitler, I., Tchernykh, A. (eds.): ISUM 2018. CCIS, vol. 948. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-10448-1","DOI":"10.1007\/978-3-030-10448-1"},{"key":"33_CR17","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.2166\/nh.2019.006","volume":"50","author":"D Ocio","year":"2019","unstructured":"Ocio, D., Beskeen, T., Smart, K.: Fully distributed hydrological modelling for catchment-wide hydrological data verification. Hydrol. Res. 50, 1520\u20131534 (2019)","journal-title":"Hydrol. Res."},{"issue":"3","key":"33_CR18","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1093\/biomet\/63.3.581","volume":"63","author":"DB Rubin","year":"1976","unstructured":"Rubin, D.B.: Inference and missing data. Biometrika 63(3), 581\u2013592 (1976). https:\/\/doi.org\/10.1093\/biomet\/63.3.581","journal-title":"Biometrika"},{"key":"33_CR19","doi-asserted-by":"publisher","unstructured":"Santoro, P., Fossati, M., Piedra Cueva, I.: Study of the meteorological tide in the R\u00edo de la Plata. Continental Shelf Res. 60, 51\u201363 (2013). https:\/\/doi.org\/10.1016\/j.csr.2013.04.018","DOI":"10.1016\/j.csr.2013.04.018"},{"key":"33_CR20","doi-asserted-by":"publisher","first-page":"2464","DOI":"10.1002\/2015WR016936","volume":"51","author":"HHG Savenije","year":"2015","unstructured":"Savenije, H.H.G.: Prediction in ungauged estuaries: an integrated theory. Water Resour. Res. 51, 2464\u20132476 (2015). https:\/\/doi.org\/10.1002\/2015WR016936","journal-title":"Water Resour. Res."},{"key":"33_CR21","unstructured":"Sellanes, L., Fossati, M., Silva, D., Ezzatti, P.: Mejoras en la implementaci\u00f3n del modelo num\u00e9rico base del pron\u00f3stico de niveles del R\u00edo de la Plata y frente mar\u00edtimo: PRONUY_RPFM. In: Mec\u00e1nica Computacional. vol.\u00a0XL, pp. 1681\u20131690 (2023)"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Silveira, V., Sarazola, I., Guti\u00e9rrez, A., Fossati, M.: Offshore surface wind map in R\u00edo de la Plata and Atlantic Ocean shelf. J. Wind Eng. Industrial Aerodyn. 258 (2025)","DOI":"10.1016\/j.jweia.2024.106002"},{"key":"33_CR23","unstructured":"Skamarock, W.C., et\u00a0al.: A description of the Advanced Research WRF model version\u00a04. Technical report. NCAR\/TN-556+STR, National Center for Atmospheric Research, Boulder, CO, USA (2019)"},{"key":"33_CR24","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2166\/hydro.2008.015","volume":"10","author":"DP Solomatine","year":"2008","unstructured":"Solomatine, D.P., Ostfeld, A.: Data-driven modelling: some past experiences and new approaches. J. Hydroinf. 10, 3\u201322 (2008)","journal-title":"J. Hydroinf."},{"key":"33_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2024.104289","volume":"153","author":"Y Sun","year":"2024","unstructured":"Sun, Y., Wang, R., Qi, C., Xu, J., Tu, Z., Yang, F.: An improved tidal prediction method using meteorological parameters and historical residual water levels. Appl. Ocean Res. 153, 104289 (2024)","journal-title":"Appl. Ocean Res."},{"issue":"3","key":"33_CR26","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/s10489-016-0785-z","volume":"45","author":"C-C Wu","year":"2016","unstructured":"Wu, C.-C., Chen, Y.-L., Liu, Y.-H., Yang, X.-Y.: Decision tree induction with a constrained number of leaf nodes. Appl. Intell. 45(3), 673\u2013685 (2016). https:\/\/doi.org\/10.1007\/s10489-016-0785-z","journal-title":"Appl. Intell."},{"key":"33_CR27","doi-asserted-by":"crossref","unstructured":"Zeng, A., Chen, M., Zhang, L., Xu, Q.: Are transformers effective for time series forecasting? In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a037, pp. 11121\u201311128 (2023)","DOI":"10.1609\/aaai.v37i9.26317"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-30488-9_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T04:10:41Z","timestamp":1782792641000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-30488-9_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,1]]},"ISBN":["9783032304872","9783032304889"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-30488-9_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,1]]},"assertion":[{"value":"1 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Braga","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2026","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"}}]}}