ESTIMATION OF TROPHIC STATUS INDEX OF LAKES USING FEED FORWARD NEURAL NETWORK MODEL WITH TWO LEARNING ALGORITHMS
Tushar Anthwal1, Akanksha Chandola Anthwal2, M. P. Thapliyal3
1Deptartment of Computer Science,Bhagwant University, Ajmer,Rajasthan, India
2Uttarakhand State Council for Science & Technology, Dehradun, India
3Department of Computer Science, HNB Garhwal (Central) University ,Srinagar Garhwal, India
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.
Keywords: Artificial neural network, Eutrophication, Trophic Status Index, Water quality, Feed Forward back propagation