Application of Extreme Learning Machine for Predicting Chlorophyll-a Concentration Inartificial Upwelling Processes
Joint Authors
Huang, Haocai
Zheng, Bofu
Wang, Yihong
Wei, Yan
Chen, Bin
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-29
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Artificial upwelling, artificially pumping up nutrient-rich ocean waters from deep to surface, is increasingly applied to stimulating phytoplankton activity.
As a proxy for the amount of phytoplankton present in the ocean, the concentration of chlorophyll a (chl-a) may be influenced by water physical factors altered in artificial upwelling processes.
However, the accuracy and convenience of measuring chl-a are limited by present technologies and equipment.
Our research intends to study the correlations between chl-a concentration and five water physical factors, i.e., salinity, temperature, depth, dissolved oxygen (DO), and pH, possibly affected by artificial upwelling.
In this paper, seven models are presented to predict chl-a concentration, respectively.
Two of them are based on traditional regression algorithms, i.e., multiple linear regression (MLR) and multivariate quadratic regression (MQR), while five are based on intelligent algorithms, i.e., backpropagation-neural network (BP-NN), extreme learning machine (ELM), genetic algorithm-ELM (GA-ELM), particle swarm optimization-ELM (PSO-ELM), and ant colony optimization-ELM (ACO-ELM).
These models provide a quick prediction to study the concentration of chl-a.
With the experimental data collected from Xinanjiang Experiment Station in China, the results show that chl-a concentration has a strong correlation with salinity, temperature, DO, and pH in the process of artificial upwelling and PSO-ELM has the best overall prediction ability.
American Psychological Association (APA)
Wei, Yan& Huang, Haocai& Chen, Bin& Zheng, Bofu& Wang, Yihong. 2019. Application of Extreme Learning Machine for Predicting Chlorophyll-a Concentration Inartificial Upwelling Processes. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1197832
Modern Language Association (MLA)
Wei, Yan…[et al.]. Application of Extreme Learning Machine for Predicting Chlorophyll-a Concentration Inartificial Upwelling Processes. Mathematical Problems in Engineering No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1197832
American Medical Association (AMA)
Wei, Yan& Huang, Haocai& Chen, Bin& Zheng, Bofu& Wang, Yihong. Application of Extreme Learning Machine for Predicting Chlorophyll-a Concentration Inartificial Upwelling Processes. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1197832
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1197832