气候变化领域集成服务门户(CCPortal): A novel approach to predict chlorophyll-a in coastal-marine ecosystems using multiple linear regression and principal component scores

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DOI	10.1016/j.marpolbul.2020.110902
	A novel approach to predict chlorophyll-a in coastal-marine ecosystems using multiple linear regression and principal component scores
	Franklin J.B.; Sathish T.; Vinithkumar N.V.; Kirubagaran R.
发表日期	2020
ISSN	0025326X
卷号	152
英文摘要	Chlorophyll-a is an established indexing marker for phytoplankton abundance and biomass amongst primary food producers in an aquatic ecosystem. Understanding and modeling the level of Chlorophyll-a as a function of environmental parameters have been found to be very beneficial for the management of the coastal ecosystems. This study developed a mathematical model to predict Chlorophyll-a concentrations based on a data driven modeling approach. The prediction model was developed using principal component analysis (PCA) and multiple linear regression analysis (MLR) approaches. The predictive success (R2) of the model was found to be ~84.8% for first approach and ~83.8% for the second approach. A final model was generated using a combined principal component scores (PCS) and MLR approach that involves fewer parameters and has a predictive ability of 83.6%. The PCS-MLR method helped to identify the relationship amongst dependent as well as predictor variables and eliminated collinearity problems. The final model is quite simple and intuitive and can be used to understand real system operations. © 2020 Elsevier Ltd
英文关键词	Chlorophyll-a; Mathematical modeling; Multiple linear regression analysis; Prediction; Principle component analysis; Seawater quality
语种	英语
scopus关键词	Aquatic ecosystems; Chlorophyll; Forecasting; Linear regression; Mathematical models; Quality control; Chlorophyll a; Chlorophyll-a concentration; Coastal marine ecosystems; Multiple linear regression analysis; Multiple linear regressions; Phytoplankton abundances; Principle component analysis; Seawater quality; Principal component analysis; chlorophyll a; chlorophyll; sea water; chlorophyll a; coastal zone; linearity; marine ecosystem; numerical model; pollution monitoring; prediction; principal component analysis; regression analysis; algal bloom; Article; concentration (parameter); environmental management; environmental monitoring; environmental stress; geography; marine environment; mathematical computing; mathematical model; multiple linear regression analysis; prediction; principal component analysis; regulatory mechanism; seashore; water temperature; ecosystem; phytoplankton; statistical model; Chlorophyll; Chlorophyll A; Ecosystem; Linear Models; Phytoplankton; Seawater
来源期刊	Marine Pollution Bulletin
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/149204
作者单位	Atal Centre for Ocean Science and Technology, National Institute of Ocean Technology, Ministry of Earth Sciences, Government of India, Port Blair, 744103, India; Marine Biotechnology Division, Ocean Science and Technology for Islands, National Institute of Ocean Technology, Ministry of Earth Sciences, Government of India, Pallikaranai, Chennai, 600100, India
推荐引用方式 GB/T 7714	Franklin J.B.,Sathish T.,Vinithkumar N.V.,et al. A novel approach to predict chlorophyll-a in coastal-marine ecosystems using multiple linear regression and principal component scores[J],2020,152.
APA	Franklin J.B.,Sathish T.,Vinithkumar N.V.,&Kirubagaran R..(2020).A novel approach to predict chlorophyll-a in coastal-marine ecosystems using multiple linear regression and principal component scores.Marine Pollution Bulletin,152.
MLA	Franklin J.B.,et al."A novel approach to predict chlorophyll-a in coastal-marine ecosystems using multiple linear regression and principal component scores".Marine Pollution Bulletin 152(2020).