Identification of Influential Sea Surface Temperature Locations and Predicting Streamflow for Six Months Using Bayesian Machine Learning Regression

Sea surface temperature (SST) has significant influence in the hydrological cycle and affects the discharge in the stream. SST is an atmospheric circulation indicator which provides the predictive information about the hydrologic variability in the region around the world. Use of right location of S...

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Veröffentlicht in:	Journal of water resource and protection 2015-02, Vol.7 (3), p.197-197
Hauptverfasser:	Shrestha, N K, Urroz, G
Format:	Artikel
Sprache:	eng
Schlagworte:	Gages Learning theory Mathematical models Regression Resource management Sea surface temperature Streams Water resources
Online-Zugang:	Volltext
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