Assessing State of the Art on Artificial Neural Network Paradigms for Level of Eutrophication Estimation of Water Bodies

With growing power of computer and blend of intelligent soft wares, the interpretation and analytical capabilities of the system had shown an excellent growth, providing intelligence solutions to almost every computing problem. In this direction here we are trying to identify how different geocomputation techniques had been implemented for estimation of parameters on water bodies so as to identify the level of contamination leading to the different level of eutrophication. The main mission of this paper is to identify state-of-art in artificial neural network paradigms that are prevailing and effective in modeling and combining spatial data for anticipation. Among this, our interest is to identify different analysis techniques and their parameters that are mainly used for quality inspection of lakes and estimation of nutrient pollutant content in it, and different neural network models that offered the forecasting of level of eutrophication in the water bodies. Different techniques are analyzed over the main steps;-assimilation of spatial data, statistical interpretation technique, observed parameters used for eutrophication estimation and accuracy of resultant data