Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes-Reference-Cited by-同舟云学术

Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes

Published:2016-06-17 Issue:55 Volume:57 Page:26783-26791
ISSN:1944-3994
Container-title:Desalination and Water Treatment
language:en
Short-container-title:Desalination and Water Treatment

Author:

Lee Gooyong¹,Bae Jungeun²,Lee Sangeun³,Jang Min⁴,Park Heekyung²

Affiliation:

1. Water Research Center, Institute of Research Management and Monitoring, University of Malaya (UM), Kuala Lumpur, Malaysia

2. Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea, Tel. ; Fax:

3. National Urban Disaster Prevention Research Center, Land Management and Urban Research Division, Korea Research Institute for Human Settlements (KRIHS), Anyang, Republic of Korea

4. Department of Environmental Engineering, Kwangwoon University, Seoul, Republic of Korea

Publisher

Informa UK Limited

Subject

Pollution,Ocean Engineering,Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/19443994.2016.1190107

Reference31 articles.

1. Towards a generic artificial neural network model for dynamic predictions of algal abundance in freshwater lakes

2. A hybrid neural–genetic algorithm for reservoir water quality management

3. Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs

4. Determination of the optimal parameters in regression models for the prediction of chlorophyll-a: A case study of the Yeongsan Reservoir, Korea

5. Exploring the influence of lake water chemistry on chlorophyll a: A multivariate statistical model analysis

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