Partial least squares regression models to predict contaminant concentrations during high or low flow of coal mine‐affected rivers-Reference-Cited by-同舟云学术

Partial least squares regression models to predict contaminant concentrations during high or low flow of coal mine‐affected rivers

Published:2022-03-04 Issue:5 Volume:38 Page:939-951
ISSN:1535-1459
Container-title:River Research and Applications
language:en
Short-container-title:River Research & Apps

Author:

Jones Catherine E.¹²^ORCID,Vicente‐Beckett Victoria¹,Chapman James³,Cozzolino Daniel⁴

Affiliation:

1. Central Queensland University, School of Health Medical and Applied Sciences Rockhampton Queensland Australia

2. Coastal Marine Ecosystems Research Centre Central Queensland University Gladstone Queensland Australia

3. School of Science RMIT University Melbourne Victoria Australia

4. Queensland Alliance for Agriculture and Food Innovation The University of Queensland, Centre for Nutrition and Food Sciences Brisbane Queensland Australia

Publisher

Wiley

Subject

General Environmental Science,Water Science and Technology,Environmental Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/rra.3959

Reference39 articles.

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2. Australian and New Zealand Governments and Australian state and territory governments (ANZG). (2018).Australian and New Zealand guidelines for fresh and marine water qualityRetrieved from Canberra:www.waterquality.gov.au/anz-guidelines

3. Australian Government Bureau of Meteorology(2021). National Water Account 2019 Fitzoy: Geographic Information. Retrieved fromhttp://www.bom.gov.au/water/nwa/2019/fitzroy/regiondescription/geographicinformation.shtml

4. Classification of River Water Quality Using Multivariate Analysis

5. Linear and nonlinear modeling for simultaneous prediction of dissolved oxygen and biochemical oxygen demand of the surface water — A case study