Classification of gray water according to purposes of using machine learning methods

Today, activities such as population growth, industrial development, climate change and unconscious consumption have increased the pressure on water resources. Due to water scarcity, the search for alternative water sources has begun. The most important of these is the reuse methods of wastewater. Gray water is untreated wastewater from bathrooms, kitchens, washing machines and dishwashers, not mixed with toilet water in any way, and can be purified and reused. Reuse of gray water is important for sustainability. Especially with the important developments in the field of artificial intelligence in recent years, the concept of artificial intelligence has become a frequently used term. It is aimed to determine areas where gray water can be used by using machine learning, which is a sub-branch of artificial intelligence. In the study, according to the mixed gray water pollution parameter values of low-income and high-income countries, the areas in which high and low removal can be used when removed by efficient treatment were determined by machine learning methods. Biological oxygen demand, pH, suspended solids, total nitrogen and total phosphorus were used as input parameters of mixed gray waters. In the study conducted only with the random forest algorithm, it was observed that 57% of the mixed gray water with a high removal efficiency rate can be used in all areas where public access is not restricted as first class.