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.

The health of water bodies across the globe is of high concern as the pollution is accelerating rigorously. With the interventions of simple technology, some significant changes could be bought up. Lakes are dying because of high Trophic Index Status which shows the eutrophication level of water bodies. Taking this into account, feed forward back propagation neural network model is used to estimate the Trophic Status Index (TSI) of lakes which could compute the value of TSI with the given parameters; pH, temperature, dissolved oxygen, Secchi disk transparency, chlorophyll and total phosphate. Two learning algorithms; Levenberg Marquardt (LM) and Broyden–Fletcher–Goldfarb–Shanno (BFGS) Quasi Newton were used to train the network, which belongs to different classes. The results were analyzed using mean square error function and further checked for the deviation from actual data. Among both the training algorithm; LM demonstrated better performance with 0.0007 average mean square error for best validation performance and BFGS Quasi Newton shows the average mean square error of 1.07.