<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Gauch</style></author><author><style face="normal" font="default" size="100%">R. Tang</style></author><author><style face="normal" font="default" size="100%">J. Mai</style></author><author><style face="normal" font="default" size="100%">B. Tolson</style></author><author><style face="normal" font="default" size="100%">S. Gharari</style></author><author><style face="normal" font="default" size="100%">J. Lin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine Learning for Streamflow Prediction: Current Status and Future Prospects</style></title><secondary-title><style face="normal" font="default" size="100%">AGU Fall Meeting Abstracts</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">2019</style></volume><pages><style face="normal" font="default" size="100%">H33L-2127</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Accurate streamflow prediction is an open challenge in hydrology. We show that approaches based on machine learning can provide more accurate predictions than physically-based models and discuss potential for improvement in hybrid approaches.
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