Akademik Veri Yönetim Sistemi
4th International Conference on Intelligent and Fuzzy Systems (INFUS), Bornova, Türkiye, 19 - 21 Temmuz 2022, cilt.504, ss.271-278
The amount of daily water demanded by individuals is used as a basic parameter in the design of infrastructure systems. The purpose of this study is to examine the daily average water consumption per capita (WCPC) values used as infrastructure basic design parameters and suggested by Turkish Standards across Turkey. Accordingly, one of the aims of this study is to reveal how accurately these standards predict WCPC values, the other one is to create a model predicting better with low error rate and trend-reflecting values. WCPC belonging to 2018 for 30 Turkish cities was introduced. According to this research study, the population weighted average WCPC was found out to be 131.9 L across the country and it ranges from 67.7 to 208 L. The population weighted average loss percent of discharge across the country is 36% while it ranges from 23% to 71%. 10 parameters for each city that had the potential to influence water consumption are following; average temperature, maximum temperature, average precipitation, humidity, water price, population, population density, sunshine duration, tourism intensity, and industry level. Since the problem is complex, the fuzzy logic method, which is a rule-based algorithm from the soft computing methods, was preferred and found suitable on the stage of creating the predictor model. The fuzzy model was formed with an expert perspective approach. The accuracy of the values proposed by the fuzzy model and the standards were measured and compared with R 2 and RMSE parameters. The coefficient of determination of Altmbilek's values, 2013 Provincial Bank, which are Turkish Standards, and the fuzzy model were found out as -4.77, -0.55, and 0.41, respectively. The poor estimation ability of the standards has revealed the need for the model that is able to make better estimation and measurement results proved the necessity of future examination of the predictions.