Prediction models for non-revenue water ratio

Öz

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

Anahtar Kelimeler

• Kriging
• Artificial neural network
• Non revenue water ratio
• Water distribution network

Gelir getirmeyen su oranı tahmin modelleri

Öz

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.

Anahtar Kelimeler

• Kriging
• Yapay Sinir Ağı
• Gelir Getirmeyen Su Oranı
• Su Dağıtım Şebekesi
• Kocaeli

Kaynakça

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