Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabuk and Bartin (Turkey)

Abstract

The determination of the rock types from which the water is recharged/discharged is an essential component of hydrochemical, hydrogeological and water pollution studies. Especially, detection of sources of groundwater contamination is very important in terms of human health and other living organism. This study aims at prediction of water pollution sources using artificial neural networks (ANNs) in Sivas, Karabuk and Bartin areas of Turkey, which have different types of rocks, agricultural activity and mining activity. In this study, a model based on ANNs was developed for forecast to the water discharging from different types of rocks and the water pollution sources in the study areas. Back propagation and Bee Algorithm (BA) were used in ANN training. For achieving the aim of the study, 14 hydrochemical data set were used. The best ANN classification of water discharging from different type of rocks was accomplished with 80 % accuracy using BA. These results indicate that the researches that are similar to this study can provide quite convenience for the assessment of groundwater pollution sources when applied on a large and regional scale.