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Sci."},{"key":"10.1016\/j.jhydrol.2015.10.038_b0710","first-page":"124","article-title":"Artificial neural networks in hydrology","volume":"1","author":"Task","year":"2000","journal-title":"By the ASCE Task Committee on Application of Artificial Neural Networks in Hydrology"},{"key":"10.1016\/j.jhydrol.2015.10.038_b0715","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s00521-013-1469-9","article-title":"Forecasting of monthly river flow with autoregressive modeling and data-driven techniques","volume":"25","author":"Terzi","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.jhydrol.2015.10.038_b0720","article-title":"Monthly evaporation forecasting using artificial neural networks and support vector machines","author":"Tezel","year":"2015","journal-title":"Theor. Appl. 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