Soyupak, S.Kilic, H.Karadirek, I. E.Muhammetoglu, H.2024-07-052024-07-052011160003-72141365-208710.2166/aqua.2011.0862-s2.0-79251501356https://doi.org/10.2166/aqua.2011.086https://hdl.handle.net/20.500.14411/1544Kilic, Hurevren/0000-0002-9058-0365; Karadirek, Ethem/0000-0003-3689-4125; KILIC, HUREVREN/0000-0003-2647-8451Artificial neural network (ANN) methodology has found some recent applications as efficient control tools for satisfying free residual chlorine (FRC) levels at critical locations of water distribution systems. This particular research was started to critically investigate the potential and applicability of the ANN approach as a tool for controlling FRC levels for complex water distribution systems supplied by high quality waters with low chlorine demands. Konyaalti Water Distribution System, operated by Antalya Water and Wastewater Administration, Turkey, has been selected as a pilot. The selected system is complex in structure and supplied with raw water which has high quality and low decay rate of chlorine. The study has shown that ANN models with high predictive power and precision can be developed for such water distribution systems, and that these models can be utilized for forecasting purposes. The data for model building should be collected properly if the developed ANN models are to be utilized as control instruments for FRC levels within water distribution systems.eninfo:eu-repo/semantics/closedAccessartificial neural networkscontrolforecastingfree residual chlorinewater distributionArticle6015160WOS:000286591900005