Development of PCA-based cluster quantile regression (PCA-CQR) framework for streamflow prediction: Application to the Xiangxi river watershed, China-Reference-Cited by-同舟云学术

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Development of PCA-based cluster quantile regression (PCA-CQR) framework for streamflow prediction: Application to the Xiangxi river watershed, China

Published:2017-02 Issue: Volume:51 Page:280-293
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Fan Y.R.,Huang G.H.,Li Y.P.,Wang X.Q.,Li Z.,Jin L.

Funder

Natural Science Foundation of China

National Key Research and Development Plan

Xiamen University of Technology Foreign Science and Technology Cooperation and Communication Foundation

Xiamen University of Technology High-level personnel Foundation

Fujian Class A Foundation

Publisher

Elsevier BV

Subject

Software

Reference54 articles.

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2. Comparison of multivariate regression and artificial neural networks for peak urban water demand forecasting: the evaluation of different ANN learning algorithms;Adamowski;J. Hydrol. Eng. (ASCE),2008

3. Uncertainty assessment in environmental risk through Bayesian networks;Ahmadi;J. Environ. Inf.,2015

4. Modelling sediment trapping by non-Submerged grass buffer strips using nonparametric supervised learning technique;Akram;J. Environ. Inf.,2015

5. Generalized regression neural network in monthly flow forecasting;Cigizoglu;Civil Eng. Environ. Syst.,2005

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