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Updated: Aug 5, 2025

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
Published on: June 13, 2020
Interaction of physico-chemical parameters with Shannon-Weaver Diversity Index based on phytoplankton diversity in
Atanu Kumar Panja1, Sonpal Vasavdutta1, Meena Choudhary1
1Analytical and Environmental Science Division & Centralized Instrument Facility, CSIR-Central Salt & Marine Chemicals Research Institute, Bhavnagar 364 002, Gujarat, India; Academy of Scientific and Innovative Research, Ghaziabad 201 002, Uttar Pradesh, India.
Abstract:
Phytoplankton acts as carbon sinks due to photosynthetic efficacy and their diversity is expressed by SWDI (Shannon-Weaver Diversity Index), which depends on water quality parameters. The coastal water of Diu was studied for three seasons, and the relationship between different parameters and SWDI was established. Subsequently, an attempt was made to build up a prediction model of SWDI based on multilayer perceptron Artificial neural network (ANN) using the R programme. Analysis shows interrelationship between the water quality parameters and phytoplankton diversity is same in linear principal component analysis (PCA) and neural network model. Variations of different parameters depend on seasonal changes. The ANN model shows that ammonia and phosphate are key parameters that influence the SWDI of phytoplankton. Seasonal variation in SWDI is related to variation in water quality parameters, as explained by both ANN and PCA. Hence, the ANN model can be an important tool for coastal environmental interaction study.
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