Determination Of Seasonal Changes In Filyos Stream Water Quality By Artificial Neural Network

Abstract

During the water cycle, substances that are contaminated in water cause physical, chemical or biological alteration of the water’s natural features, that leads to water pollution and therefore causes the environmental balance to deteriorate over time. For this reason, it is important to investigate the water quality in rivers and water reservoirs which are close to settlement areas. In this study, surface water quality measurements were carried out at five observation stations along the main line of the Filyos stream, which forms the largest sub-basin in the western Karadeniz Basin, at intervals of thirty days in one year period. Water quality parameters (Chemical Oxygen Demand (COD), Calcium (Ca+2), Aluminum (Al+3), Manganese (Mn+2), Iron (Fe+3), Chromium (Cr+3), Lead (Pb+4), Zinc (Zn+2), Turbidity and Suspended Solids) seasonally measured and the analyzes were performed in the laboratory according to Standard Methods 2005 (APHA, 1998). After, estimation of the turbidity parameter based on parameters of chromium, chemical oxygen demand, iron, aluminum, suspended solids, manganese, zinc, lead and calcium was performed by artificial neural networks. The model results are shown in tables for each station separately and the topologies with the best performance is charted.