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    Estimation of parameters affecting water quality using data mining algorithms
    (Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi, 2025) Kavak, Elif; Kaya Keleş, Mümine
    Achieving a sustainable life is one of the most important issues today. In order for future generations to achieve this life, the United Nations (UN) has published the Sustainable Development Goals (SDGs). Among these, the 6th SDG under the title of "Clean Water and Sanitation" is of critical importance in terms of human health, hygiene and protection of ecosystems. Therefore, it reveals the necessity of sustainable management and effective monitoring of water resources. In this context, data mining applications and regression models were developed in the study carried out in order to monitor water quality and predict future changes. Within the scope of the study, five separate data sets were created by bringing together the parameters affecting water quality obtained from drinking water analyses of Adana, Mersin, İzmir, Sakarya and İstanbul provinces of Turkey. Water Quality Index (WQI) was calculated using various physical and chemical parameters. Various algorithms such as Linear Regression (LR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), Decision Tree (DT), Random Forest (RF), Ridge Regression (RR) and Lasso were evaluated comparatively. As a result of the study, it was seen that ANN, SVR and LR models were effective for water quality management among the models evaluated with two different evaluation metrics.