Evaluation of heavy metal parameters of Lake İznik by artificial neural networks

In this study, water quality parameter measurements were taken 4 starting point from İznik lake which are Karasu, Kırandere, Olukdere, Sölöz and 1 outpoint which is Karsak Stream in the 2015 to 2021. The effects on heavy metals Sn, As, Fe, Mn, Cu, Pb, Sb, Al, B, Cr, Cd, Ni, Zn, Se, Ag on pH, conductivity, temperature, biological oxygen need, chemical oxygen need, dissolved oxygen and colorful physcochemical parameters. Various attempts were made to predict heavy metals and an artificial neural network (ANN) was used for model studies. IBM SPSS statistical 23 software was used as the model. The sum of squares of error (SSE) and coefficient of determination (R2) were used to evaluate the amount of error in the performance evaluation of heavy metal values. The results showed that the coefficient of determination was mostly close to 1 and the ANN analysis showed that the pollution estimation of heavy metal parameters could be realized. Thus, it has been seen that the ANN model is an estimation tool that can be used effectively to describe heavy metal pollution in the lake