Discrete principal-monotonicity inference for hydro-system analysis under irregular nonlinearities, data uncertainties, and multivariate dependencies. Part I: methodology development-Reference-Cited by-同舟云学术

Discrete principal-monotonicity inference for hydro-system analysis under irregular nonlinearities, data uncertainties, and multivariate dependencies. Part I: methodology development

Published:2016 Issue: Volume: Page:
ISSN:0885-6087
Container-title:Hydrological Processes
language:en
Short-container-title:Hydrol. Process.

Author:

Cheng Guanhui¹,Dong Cong²,Huang Guohe¹,Baetz Brian W.³,Han Jingcheng⁴

Affiliation:

1. Faculty of Engineering and Applied Science; University of Regina; Regina Saskatchewan S4S 0A2 Canada

2. Institute for Energy, Environment and Sustainable Communities; University of Regina; Regina Saskatchewan S4S 7H9 Canada

3. Civil Engineering; McMaster University; Hamilton Ontario CAN L8S4L8 Canada

4. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering; Tsinghua University; Beijing CN 100084 China

Funder

the Natural Sciences Foundation

111 Project

Natural Sciences and Engineering Research Council of Canada

Publisher

Wiley

Subject

Water Science and Technology

Reference83 articles.

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2. An introduction to the European hydrological system-Système Hydrologique Européen “SHE”, 2: structure of a physically-based distributed modelling system;Abbott;Hydrology,1986b

3. Neural Networks for Hydrological Modeling

4. Uncertainty assessment in environmental risk through bayesian networks;Ahmadi;Journal of Environmental Informatics,2015

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