{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T22:02:55Z","timestamp":1772834575897,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319961323","type":"print"},{"value":"9783319961330","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-96133-0_28","type":"book-chapter","created":{"date-parts":[[2018,7,7]],"date-time":"2018-07-07T11:54:57Z","timestamp":1530964497000},"page":"369-382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Flow Prediction Versus Flow Simulation Using Machine Learning Algorithms"],"prefix":"10.1007","author":[{"given":"Milan","family":"Cisty","sequence":"first","affiliation":[]},{"given":"Veronika","family":"Soldanova","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,7,8]]},"reference":[{"issue":"2","key":"28_CR1","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1061\/(ASCE)1084-0699(2000)5:2(115)","volume":"5","author":"ASCE Task Committee on Application of Artificial Neural Networks in Hydrology","year":"2000","unstructured":"ASCE Task Committee on Application of Artificial Neural Networks in Hydrology: Artificial neural networks in hydrology. I: preliminary concepts. J. Hydrol. Eng. 5(2), 115\u2013123 (2000)","journal-title":"J. Hydrol. Eng."},{"key":"28_CR2","doi-asserted-by":"publisher","unstructured":"Papacharalampous, G.A., Tyralis, H., Koutsoyiannis, D.: Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes. Preprints 2017, 2017100133. https:\/\/doi.org\/10.20944\/preprints201710.0133.v1","DOI":"10.20944\/preprints201710.0133.v1"},{"issue":"1","key":"28_CR3","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/S1364-8152(99)00007-9","volume":"15","author":"HR Maier","year":"2000","unstructured":"Maier, H.R., Dandy, G.C.: Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Model Softw. 15(1), 101\u2013124 (2000)","journal-title":"Environ. Model Softw."},{"key":"28_CR4","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1016\/j.jhydrol.2015.10.038","volume":"530","author":"ZM Yaseen","year":"2015","unstructured":"Yaseen, Z.M., El-Shafie, A., Jaafar, O., Afan, H.A., Sayl, K.N.: Artificial intelligence based models for stream-flow forecasting: 2000\u20132015. J. Hydrol. 530, 829\u2013844 (2015)","journal-title":"J. Hydrol."},{"key":"28_CR5","unstructured":"Szalai, S., Spinoni, J., Galos, B., Bessenyei, M., Molar, P., Szentimrey, T.: Use of regional database for climate change and drought. In: 5th IDRC Davos 2014. Global Risk Forum GRF Davos (2014)"},{"key":"28_CR6","unstructured":"Viglione, A., Parajka, J.: TUWmodel: Lumped Hydrological Model for Education Purposes. R package version 0.1-8 (2016). https:\/\/CRAN.R-project.org\/package=TUWmodel"},{"issue":"1\u20134","key":"28_CR7","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/S0022-1694(97)00041-3","volume":"201","author":"G Lindstr\u00f6m","year":"1997","unstructured":"Lindstr\u00f6m, G., et al.: Development and test of the distributed HBV-96 hydrological model. J. Hydrol. 201(1\u20134), 272\u2013288 (1997)","journal-title":"J. Hydrol."},{"issue":"4","key":"28_CR8","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1002\/hyp.6253","volume":"21","author":"J Parajka","year":"2007","unstructured":"Parajka, J., Merz, R., Bl\u00f6schl, G.: Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments. Hydrol. Process. 21(4), 435\u2013446 (2007)","journal-title":"Hydrol. Process."},{"issue":"3","key":"28_CR9","doi-asserted-by":"publisher","first-page":"151","DOI":"10.4236\/ojmh.2016.63013","volume":"6","author":"J Boisvert","year":"2016","unstructured":"Boisvert, J., El-Jabi, N., St-Hilaire, A., El Adlouni, S.E.: Parameter estimation of a distributed hydrological model using a genetic algorithm. Open J. Mod. Hydrol. 6(3), 151\u2013167 (2016)","journal-title":"Open J. Mod. Hydrol."},{"issue":"1","key":"28_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"3","key":"28_CR11","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw, A., Wiener, M.: Classification and regression by randomForest. R News 2(3), 18\u201322 (2002)","journal-title":"R News"},{"key":"28_CR12","unstructured":"R Core Team: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2017). https:\/\/www.R-project.org\/"},{"issue":"5","key":"28_CR13","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":"28_CR14","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. ACM (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"28_CR15","unstructured":"XGBoost Homepage. https:\/\/xgboost.readthedocs.io\/en\/latest\/. Accessed 16 Mar 2018"},{"key":"28_CR16","unstructured":"Allaire, J.J., Chollet, F.: keras: R Interface to \u2018Keras\u2019. R package version 2.1.4 (2018). https:\/\/CRAN.R-project.org\/package=keras"},{"issue":"3","key":"28_CR17","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1016\/0022-1694(70)90255-6","volume":"10","author":"JE Nash","year":"1970","unstructured":"Nash, J.E., Sutcliffe, J.V.: River flow forecasting through conceptual models part I-A discussion of principles. J. Hydrol. 10(3), 282\u2013290 (1970)","journal-title":"J. Hydrol."},{"issue":"1\u20132","key":"28_CR18","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.jhydrol.2009.08.003","volume":"377","author":"HV Gupta","year":"2009","unstructured":"Gupta, H.V., et al.: Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J. Hydrol. 377(1\u20132), 80\u201391 (2009)","journal-title":"J. Hydrol."},{"key":"28_CR19","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.jhydrol.2012.01.011","volume":"424","author":"H Kling","year":"2012","unstructured":"Kling, H., Fuchs, M., Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. J. Hydrol. 424, 264\u2013277 (2012)","journal-title":"J. Hydrol."},{"issue":"1","key":"28_CR20","first-page":"49","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Stacked regressions. Mach. Learn. 24(1), 49\u201364 (1996)","journal-title":"Mach. Learn."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Data Mining in Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-96133-0_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T11:29:41Z","timestamp":1709810981000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-96133-0_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319961323","9783319961330"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-96133-0_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"8 July 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLDM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning and Data Mining in Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New York, NY","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mldm2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mldm.de\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}