Şamandar, Ayhan2020-04-302020-04-3020101992-2248https://hdl.handle.net/20.500.12684/4305WOS: 000279559800005Artificial neural networks (ANNs) have been recently used intensively in the water resource management and planning. As the demand on the clean water increases, the estimation of important parameters using ANNs to evaluate the surface water quality has become an importance practice. In this study, feature selection methods, which are important tools in data mining, are used to shed light into a further understanding in the correlation between measured parameters of water quality variables and dissolved oxygen level. The aim is to enhance the generalization ability of ANNs in prediction of dissolved oxygen level. Three different feature selection methods are utilized to discern the inherent correlation in the water quality variables in a given data set collected from Melen River, Turkey. Results show that, a small set of variable can be identified, which effectively improves the generalization ability of ANNs.eninfo:eu-repo/semantics/closedAccessWater qualityartificial neural networksMelen riverrankingArticle51112541259WOS:000279559800005N/AQ3