TY - JOUR T1 - Gelir getirmeyen su oranı tahmin modelleri TT - Prediction models for non-revenue water ratio AU - Kızılöz, Burak AU - Şişman, Eyüp PY - 2021 DA - January Y2 - 2020 DO - 10.28948/ngumuh.789694 JF - Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi JO - NÖHÜ Müh. Bilim. Derg. PB - Nigde Omer Halisdemir University WT - DergiPark SN - 2564-6605 SP - 276 EP - 283 VL - 10 IS - 1 LA - tr AB - Bu araştırmada Gelir Getirmeyen Su Oranı (GGSO) tahminleri, Kocaeli’nin en fazla su kaybı yaşanan altı ilçesinin 2018 ve2019 yıllarına ait iki yıllık verisi dikkate alınarak ve tüketilen su miktarı, şebeke uzunluğu, servis bağlantı uzunluğu, toplam şebeke uzunluğu, şebeke yaşı ve şebeke basıncı ana parametreleri kullanılarak gerçekleştirilmiştir. Model tahminleri iki girdi ve tek çıktılı Yapay Sinir Ağı (YSA) modelleri ve Kriging yöntemi ile gerçekleştirilmiştir. Modellerde toplam şebeke uzunluğu ve iki girdili YSA model kombinasyonlarında ise, servis bağlantı uzunluğu ilk kez bu araştırmada model girdisi olarak kullanılmıştır. Yöntemlerin model çıktı performansları R2 ve HKOK performans ölçütleri üzerinden değerlendirilmiştir. Sonuç olarak; Kriging yöntemi ile gerçekleştirilen modellerin tahmin performansları YSA yöntemine göre oldukça iyidir. Kriging tekniği ile oluşturulan GGSO tahmin model çıktılarının değerlendirilmesi ve yorumlanması elde edilen tahmin haritaları sayesinde daha kolay yapılabilirken, kapalı model yapısına sahip olan YSA model sonuçlarında bu durum nitelikli uzmanlık gerektirmektedir. KW - Kriging KW - Yapay Sinir Ağı KW - Gelir Getirmeyen Su Oranı KW - Su Dağıtım Şebekesi KW - Kocaeli N2 - In this study, Non-Revenue Water Rate (NRWR) predictions have been made by taking into account two-year data (2018 and 2019) of Kocaeli and using the main parameters of consumed water amount, network length, service connection length, total network length, network age and network pressure in only six districts with the highest water loss. Model predictions have been made by both Artificial Neural Network (ANN) models with two inputs one output and Kriging method. In this study, the total network length for all model combinations and the service connection length for ANN combinations with two inputs have been used as a model input for the first time. The model output performances of the above-mentioned methods have been evaluated in accordance with R2 and HKOK criteria. In conclusion, it is obvious according to the results that the model prediction performances made by Kriging method are better than the other one (ANN method). On the other hand, while evaluating the NRWR prediction model outputs established by Kriging method is easier through the prediction charts, this requires a specialized skill set for evaluating the ANN results CR - [1] R. Liemberger and A. Wyatt, Quantifying the global non-revenue water problem. Water Science and Technology: Water Supply, 19 (10), 831–837, 2019. https://doi.org/10.2166/ws.2018.129. CR - [2] IBNET, The international benchmarking networks. https://database.ibnet.org/Reports/Indicators/HeatMap?itemId=27, Accessed 06 November 2020. CR - [3] AWWA, Best practice in water loss control: improved concepts for 21st century water management, American Water Works Association. https://www. awwa.org/Portals/0/AWWA/ETS/Resources/WLCFlyerFinal.pdf?ver=2015-02-10-083650-287. 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Journal of Environmental Radioactivity, (150),132–144, 2015. https://doi.org/10.1016/j.jenvrad.2015.08.011. UR - https://doi.org/10.28948/ngumuh.789694 L1 - https://dergipark.org.tr/en/download/article-file/1271200 ER -