Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM)-Reference-Cited by-同舟云学术

Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM)

Published:2019-06-19 Issue:2 Volume:19 Page:149-156
ISSN:1571-5124
Container-title:International Journal of River Basin Management
language:en
Short-container-title:International Journal of River Basin Management

Author:

Leong Wei Cong¹,Bahadori Alireza²,Zhang Jie³,Ahmad Z.¹^ORCID

Affiliation:

1. School of Chemical Engineering, University Sains Malaysia, Nibong Tebal, Penang, Malaysia

2. School of Environment, Science and Engineering, Southern Cross University, Lismore, NSW, Australia

3. School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, UK

Funder

Universiti Sains Malaysia

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/15715124.2019.1628030

Reference15 articles.

1. Amornsamankul, S., and Kraipeerapun, P., 2013. Prediction of WQI for the Chin River using an ensemble of support vector regression and complementary neural networks. In: Recent advances in information science, 25–27 June 2013. Dubronik, 36–40.

2. null

3. Rapid detection of chemical oxygen demand using least square support vector machines

4. Khuan, L., Hamzah, N., and Jailani, R., 2002. Prediction of water quality index (WQI) based on Artificial Neural Network (ANN). In: Student conference on research and development proceedings, 16–17 July 2002. Shah Alam.

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