TO ANALYZE THE EUTROPHICATION PROCESS AND ITS EFFECTS ON INTERACTING AQUATIC POPULATION- FEM AND ANN
In the aquatic environment, the level of eutrophication is increasing. It is critical to precisely assess eutrophication in order to treat it appropriately. Moreover, the assessment of eutrophication has not been satisfactorily resolved since it is fraught with uncertainties. Hence, we have considered a lake that has been eutrophicated due to an overabundance of algae and other biological species induced by nutrient flow from home drainage, water runoff, and other sources, as well as nutrients created from detritus. The variables bilinear interactions such as cumulative nutrient concentrations, algal (phytoplankton) and fish population densities, detritus density, and dissolved oxygen concentration are addressed. ANN model has been established by utilizing the pertinent parameters data set attained by finite element method. Here, 80% of the data used in the development of the multilayer perceptron network, 10% of the data has been used for training the model and 10% for testing. The study's outcomes revealed that the built ANN models can generate accurate predictions.
Eutrophication Process; Artificial Neural Networking; Finite Element Analysis.