Improving predictions of hydrological low-flow indices in ungaged basins using machine learning-Reference-Cited by-同舟云学术

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Improving predictions of hydrological low-flow indices in ungaged basins using machine learning

Published:2018-03 Issue: Volume:101 Page:169-182
ISSN:1364-8152
Container-title:Environmental Modelling & Software
language:en
Short-container-title:Environmental Modelling & Software

Author:

Worland Scott C.^ORCID,Farmer William H.,Kiang Julie E.

Funder

U.S. Environmental Protection Agency

U.S. Geological Survey (USGS) Water Availability and Use Science Project

Publisher

Elsevier BV

Subject

Ecological Modeling,Environmental Engineering,Software

Reference104 articles.

1. An introduction to kernel and nearest-neighbor nonparametric regression;Altman;Am. Statistician,1992

2. Estimating 7q10 confidence limits from data: a bootstrap approach;Ames;J. Water Resour. Plann. Manag.,2006

3. Map correlation method: selection of a reference streamgage to estimate daily streamflow at ungaged catchments;Archfield;Water Resour. Res.,2010

4. Predictions in ungauged basins–where do we stand?;Blöschl;Proc. IAHS,2016

5. Comparing and combining physically-based and empirically-based approaches for estimating the hydrology of ungauged catchments;Booker;J. Hydrol.,2014

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