@article{article_866497, title={The Performance of Artificial Neural Network Approaches to Estimate the Nitrate Concentration in Groundwater}, journal={Avrupa Bilim ve Teknoloji Dergisi}, pages={873–879}, year={2021}, DOI={10.31590/ejosat.866497}, author={Coban, Asli}, keywords={Groundwater, Nitrate Pollution, Artificial Neural Network, Regression, BPNN, GRNN}, abstract={The estimation of the pollution concentration in groundwater is important, since it is one of the key resources of water supply. Nitrate (NO3-N) is one of the well-known indicator parameters in groundwater pollution. Using historical data, artificial neural networks can be utilized to estimate the nitrate concentration in groundwater. In this study, a sample dataset, which is derived from a survey analysis in the literature, is used to estimate the nitrate concentration of groundwater (i.e., target parameter) with respect to six different well characteristics (i.e., input parameters). The effect of different hydrogeological parameters of the wells on the nitrate concentration is focused for the first time in this study. The performance of two different ANN approaches, namely BPNN and GRNN, is evaluated comparatively by means of their regression performances. Considering regression results of ANN models, it can be concluded that the GRNN (R=0.99) algorithm works slightly better than the BPNN (R=0.98) algorithm with this dataset. Correlation results indicate that the most important characteristics of the wells to estimate the nitrate pollution are the well depth, depth below water table, clay above screen, and depth to well screen, respectively. Moreover, all these characteristics are inversely related to nitrate concentration of the well.}, number={27}, publisher={Osman SAĞDIÇ}