Interaction of physico-chemical parameters with Shannon-Weaver Diversity Index based on phytoplankton diversity in coastal water of Diu, India. (May 2023) - Panja, Atanu Kumar; Vasavdutta, Sonpal; Choudhary, Meena; Thiyagarajan, Indirapriyatharsini; Shinde, Ambika H.; Ray, Sanak; Sahoo, Tarini P.; Chatterjee, Shruti; Thorat, Ravikumar B.; Madhava, Anil Kumar; Haldar, Soumya
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. Highlights: Factors influencing the phytoplankton diversity are nitrite, nitrite, ammonium and phosphate. SWDI influencing parameters are estimated using the PCA analysis. MLP-ANN has been used for SWDI model prediction Prediction trends were similar for PCA and MLP-ANN model