2007
DOI: 10.1007/s10661-007-9829-5
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Step-up multiple regression model to compute Chlorophyll a in the coastal waters off Cochin, southwest coast of India

Abstract: The interaction effects of abiotic processes in the production of phytoplankton in a coastal marine region off Cochin are evaluated using multiple regression models. The study shows that chlorophyll production is not limited by nutrients, but their physiological regulations (responses to nutrients, pH, temperature and salinity) are mainly responsible for the increased biological production. The model explaining 77% of variability for chlorophyll a production is indicative of preconditioning of the coastal wate… Show more

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Cited by 10 publications
(7 citation statements)
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“…Monitoring programs result in extensive and complex databases, covering several aspects which are difficult to analyze and interpret due to the interrelationship among the different variables (Huang et al, 2011). Given the complexity and dynamism of ecological interactions, especially in wastewater biological treatment systems, studies are increasingly searching for the aid of sophisticated techniques such as multivariate statistics (Balachandran et al, 2008). The multivariate statistics is one of the main tools to relate environmental variables and the density of phytoplankton populations (Thangaradjou et al, 2012).…”
Section: Statistical Analysesmentioning
confidence: 99%
“…Monitoring programs result in extensive and complex databases, covering several aspects which are difficult to analyze and interpret due to the interrelationship among the different variables (Huang et al, 2011). Given the complexity and dynamism of ecological interactions, especially in wastewater biological treatment systems, studies are increasingly searching for the aid of sophisticated techniques such as multivariate statistics (Balachandran et al, 2008). The multivariate statistics is one of the main tools to relate environmental variables and the density of phytoplankton populations (Thangaradjou et al, 2012).…”
Section: Statistical Analysesmentioning
confidence: 99%
“…Hence the growing and declining condition of algal bloom can be described by the spatial and temporal variation of chlorophyll a. There are many study methods to the relationship between chlorophyll a and physicochemical factors such as stepwise multiple regression analysis [2], grey relative analysis [3] and artificial neural network [4]. Chlorophyll a can be related to the environmental parameters by means of linear regression, though it provides only the prediction efficiency of a single factor at a time [5][6][7].…”
Section: Introductionmentioning
confidence: 99%
“…But the multivariate interaction and nonlinear process in the algal bloom were not considered in the previous work. So this present paper aims to study: (1) controlling and interactive factors of chlorophyll a, (2) nonlinear interrelationship between physicochemical parameters and chlorophyll a of spring algal bloom in Xiangxi Bay. In order to achieve this, stepwise multiple binomial regression method and grey relative analysis are adopted.…”
Section: Introductionmentioning
confidence: 99%
“…Another study using multiple regression models off Kochi revealed that chlorophyll production depends not only on nutrients, but also on their physiological regulations like responses to nutrients, pH, temperature and salinity. As the computed and measured chlorophyll-a values are in good agreement, the step-up multiple regression model can be applied for the coastal waters to understand the influence of environmental variables on the production of phytoplankton [62]. Carbon-based ocean productivity and phytoplankton physiology were investigated by using satellite data [63].…”
Section: Biogeochemical Processes In Mudbanksmentioning
confidence: 99%