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Journals / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2026 / Cilt: 23 Sayı: 2

MONTHLY WATER DEMAND FORECASTING BY ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH

UYARLAMALI SİNİRSEL BULANIK MANTIK YAKLAŞIMI İLE AYLIK SU TÜKETİMİNİN TAHMİNİ

Mahmut Fırat
M. Ali Yurdusev
Mutlu Mermer
Pages
449–457
Publication Date
2013-03-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results indicated that ANFIS can be applied successfully for monthly water demand forecasting .

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, an adaptive Neuro-Fuzzy inference system (ANFIS) is used to forecast monthly water use from several socio-economic and climatic factors, which affect water use.
📊
Key Findings & Data:Totally 108 data sets are collected and data sets are divided into two subsets, training and testing.
💡
Conclusion & Impact:The results indicated that ANFIS can be applied successfully for monthly water demand forecasting .
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Su tüketimi · su tüketim tahmini · uyarlamalı sinirsel bulanık mantık.

Abstract

In this study, an adaptive Neuro-Fuzzy inference system (ANFIS) is used to forecast monthly water use from several socio-economic and climatic factors, which affect water use. Totally 108 data sets are collected and data sets are divided into two subsets, training and testing. The models consisting of the combination of the independent variables are constructed and the best fit input structure is investigated. The performance of ANFIS models in training and testing sets are compared with the observations and the best fit model forecasting model is identified. For this purpose, some criteria of performance evaluation such as, Root Mean Square Error (RMSE), efficiency (E) and correlation coefficient (CORR) are calculated for all models. Then, the best fit models are also trained and tested by Multiple Regression (MR). The results of models are compared to get more reliable comparison. The results indicated that ANFIS can be applied successfully for monthly water demand forecasting .

Özet

Bu çalışmada, Uyarlamalı Sinirsel Bulanık Mantık (USBM) yaklaşımı ile ekonomik ve iklim şartları gibi faktörlere bağlı olarak değişen aylık su kullanımının tahmin edilmesi amaçlanmaktadır. Bunun için, toplam 108 adet veri toplanmış ve eğitim ve test olarak iki gruba ayrılmıştır. Çeşitli giriş değişkenlerin değişik bileşenlerinden oluşan modeller kurulmuş ve aylık su tüketiminin tahmini için en uygun model yapısı araştırılmıştır. Kurulan her bir model USBM yöntemi ile eğitilerek test edilmiş ve gözlem değerleri ile karşılaştırılarak en uygun model yapısı belirlenmiştir. Bu amaçla, Korelasyon Katsayısı (CORR), Verimlilik (E) ve Karesel Hataların Ortalamasının Karekökü (RMSE) gibi çeşitli performans değerlendirme ölçütleri hesaplanmıştır. Daha sonra, daha doğru ve güvenilir bir karşılaştırma ve değerlendirme yapmak için, en uygun model aynı veri seti kullanılarak Çoklu Regresyon Yöntemi (ÇRY) ile çözülmüş ve test edilmiştir. Her iki yöntem sonuçları karşılaştırılarak performansları değerlendirilmiştir. Elden edilen sonuçlar, USBM yönteminin aylık su tüketimi tahmininde regresyon yöntemine göre daha iyi sonuçlar verdiğini ve su tüketimi tahmininde etkili bir şekilde uygulanabileceğini göstermiştir.

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Cite This Article

Fırat, M., Yurdusev, M. A., Mermer, M. (2013). MONTHLY WATER DEMAND FORECASTING BY ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM APPROACH. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 23 Sayı: 2, 449–457. https://ajindex.com/articles/1078847

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